README.md

# learning ![](https://hits.dwyl.com/amitness/learning.svg)


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  <img src="https://i.imgur.com/A5J6Qdb.png" width="316" height="292"/>
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**Learning Philosophy**: 
- [The Power of Tiny Gains](https://jamesclear.com/continuous-improvement)
- [Master Adjacent Disciplines](http://www.effectiveengineer.com/blog/master-adjacent-disciplines)
- [T-shaped skills](https://en.wikipedia.org/wiki/T-shaped_skills)
- [Data Scientists Should Be More End-to-End](https://eugeneyan.com/writing/end-to-end-data-science/)
- [Just in Time Learning](https://www.developgoodhabits.com/just-in-time-learning/)

## Index

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- [Have basic business understanding](#have-basic-business-understanding)
- [Be able to frame an ML problem](#be-able-to-frame-an-ml-problem)
- [Be familiar with data ethics](#be-familiar-with-data-ethics)
- [Be able to import data from multiple sources](#be-able-to-import-data-from-multiple-sources)
- [Be able to setup data annotation efficiently](#be-able-to-setup-data-annotation-efficiently)
- [Be able to manipulate data with Numpy](#be-able-to-manipulate-data-with-numpy)
- [Be able to manipulate data with Pandas](#be-able-to-manipulate-data-with-pandas)
- [Be able to manipulate data in spreadsheets](#be-able-to-manipulate-data-in-spreadsheets)
- [Be able to manipulate data in databases](#be-able-to-manipulate-data-in-databases)
- [Be able to use Linux tools](#be-able-to-use-linux-tools)
- [Be able to perform feature engineering](#be-able-to-perform-feature-engineering)
- [Be able to experiment in a notebook](#be-able-to-experiment-in-a-notebook)
- [Be able to visualize data](#be-able-to-visualize-data)
- [Be able to do literature review using research papers](#be-able-to-do-literature-review-using-research-papers)
- [Be able to model problems mathematically](#be-able-to-model-problems-mathematically)
- [Be able to structure machine learning projects](#be-able-to-structure-machine-learning-projects)
- [Be able to version control code](#be-able-to-version-control-code)
- [Be able to use version control data](#be-able-to-use-version-control-data)
- [Be familiar with fundamental of ML and DL](#be-familiar-with-fundamental-of-ml-and-dl)
- [Be able to implement models in scikit-learn](#be-able-to-implement-models-in-scikit-learn)
- [Be able to implement models in Tensorflow and Keras](#be-able-to-implement-models-in-tensorflow-and-keras)
- [Be able to implement models in PyTorch](#be-able-to-implement-models-in-pytorch)
- [Be able to implement models using cloud services](#be-able-to-implement-models-using-cloud-services)
- [Be able to apply unsupervised learning algorithms](#be-able-to-apply-unsupervised-learning-algorithms)
- [Be able to implement NLP models](#be-able-to-implement-nlp-models)
- [Be familiar with Recommendation Systems](#be-familiar-with-recommendation-systems)
- [Be able to implement computer vision models](#be-able-to-implement-computer-vision-models)
- [Be able to model graphs and network data](#be-able-to-model-graphs-and-network-data)
- [Be able to implement models for timeseries and forecasting](#be-able-to-implement-models-for-timeseries-and-forecasting)
- [Be familiar with Reinforcement Learning](#be-familiar-with-reinforcement-learning)
- [Be able to optimize performance metric](#be-able-to-optimize-performance-metric)
- [Be familiar with literature on model interpretability](#be-familiar-with-literature-on-model-interpretability)
- [Be able to optimize model size for production](#be-able-to-optimize-model-size-for-production)
- [Be able to write unit tests](#be-able-to-write-unit-tests)
- [Be able to serve models via APIs](#be-able-to-serve-models-via-apis)
- [Be able to build interactive UI for models](#be-able-to-build-interactive-ui-for-models)
- [Be able to deploy model to production](#be-able-to-deploy-model-to-production)
- [Be able to perform load testing](#be-able-to-perform-load-testing)
- [Be able to perform A/B testing](#be-able-to-perform-ab-testing)
- [Be proficient in Python](#be-proficient-in-python)
- [Be familiar with compiled languages](#be-familiar-with-compiled-languages)
- [Have a general understanding of other parts of the stack](#have-a-general-understanding-of-other-parts-of-the-stack)
- [Be familiar with fundamental Computer Science concepts](#be-familiar-with-fundamental-computer-science-concepts)
- [Be able to apply proper software engineering process](#be-able-to-apply-proper-software-engineering-process)
- [Be able to efficiently use a text editor](#be-able-to-efficiently-use-a-text-editor)
- [Be able to communicate and collaborate well](#be-able-to-communicate-and-collaborate-well)
- [Be familiar with the hiring pipeline](#be-familiar-with-the-hiring-pipeline)
- [Broaden Perspective](#broaden-perspective)

<!-- tocstop -->

### Have basic business understanding
- [ ] [Book: Delivering Happiness](https://www.amazon.com/Delivering-Happiness-Profits-Passion-Purpose/dp/0446576220)
- [ ] [Book: Good to Great: Why Some Companies Make the Leap...And Others Don't](https://www.amazon.com/Good-Great-Some-Companies-Others-ebook/dp/B0058DRUV6)
- [ ] [Book: Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams](https://www.amazon.com/Hello-Startup-Programmers-Building-Technologies/dp/1491909900)
- [ ] [Book: How Google Works](https://www.howgoogleworks.net)
- [ ] [Book: Learn to Earn: A Beginner's Guide to the Basics of Investing and Business](https://www.goodreads.com/book/show/817589.Learn_to_Earn)
- [ ] [Book: Rework](https://www.goodreads.com/book/show/6732019-rework)
- [ ] [Book: The Airbnb Story](https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669)
- [ ] [Book: The Personal MBA](https://www.amazon.com/Personal-MBA-Master-Art-Business/dp/1591845572)
- [ ] [Facebook: Digital marketing: get started](https://learn.fb.com/skillset/marketing-started)
- [ ] [Facebook: Digital marketing: go further](https://learn.fb.com/skillset/marketing-further)
- [ ] [Google Analytics for Beginners](https://analytics.google.com/analytics/academy/course/6)
- [ ] [Google: Fundamentals of Digital Marketing](https://learndigital.withgoogle.com/digitalgarage/course/digital-marketing)
- [ ] [Moz: The Beginner's Guide to SEO](https://moz.com/beginners-guide-to-seo)
- [ ] [Smartly: Marketing Fundamentals](https://smart.ly/course/abd47e21-4b59-4ccf-910d-fb1c18b2e9df)
- [ ] [Treehouse: SEO Basics](https://teamtreehouse.com/library/seo-basics)
- [ ] [Udacity: App Monetization](https://www.udacity.com/course/app-monetization--ud518)
- [ ] [Udacity: App Marketing](https://www.udacity.com/course/app-marketing--ud719)
- [ ] [Udacity: Get Your Startup Started](https://www.udacity.com/course/get-your-startup-started--ud806)
- [ ] [Udacity: How to Build a Startup](https://www.udacity.com/course/how-to-build-a-startup--ep245)
- [ ] [Youtube: SEO Unlocked](https://youtu.be/Q_lySNxCag0?list=PLJR61fXkAx11Oi6EpqJ9Es4rVOIZhwlSG)
	- [ ] [Welcome to the SEO Unlocked](https://www.youtube.com/watch?v=Q_lySNxCag0) `0:10:09`
	- [ ] [Introduction to SEO and Why It's Important](https://www.youtube.com/watch?v=pIbQfOcsEsE) `0:10:29`
	- [ ] [Keyword Research Part 1](https://www.youtube.com/watch?v=CYicoAcAi0A) `0:19:20`
	- [ ] [Keyword Research Part 2](https://www.youtube.com/watch?v=U655ixy-sdE) `0:09:56`
	- [ ] [On-page and technical SEO Part 1](https://www.youtube.com/watch?v=PXDPqXHLSOY) `0:22:58`
	- [ ] [On-page and technical SEO Part 2](https://www.youtube.com/watch?v=utLaKIJKygA) `0:12:16`
	- [ ] [Mastering Technical SEO Audits](https://www.youtube.com/watch?v=k04rHijEPSw) `0:16:35`
	- [ ] [Content Marketing Part 1](https://www.youtube.com/watch?v=zks1MFJsyUc) `0:24:09`
	- [ ] [Advanced Content Marketing Tactics](https://www.youtube.com/watch?v=cJbKV2_B-xc) `0:09:54`
	- [ ] [The 10 Commandments of Content Marketing](https://www.youtube.com/watch?v=28MeI9YccNY) `0:19:01`
	- [ ] [How to Edit Your Content For SEO](https://www.youtube.com/watch?v=-1hw7akbdBQ) `0:10:59`
	- [ ] [Discover Your Competitive Strategy](https://www.youtube.com/watch?v=aNBotpi7J98) `0:09:12`
	- [ ] [Over 4 Million Backlinks Built With This Simple Process](https://www.youtube.com/watch?v=h-4ksx-b_ns) `0:11:09`
	- [ ] [How to Get POWERFUL Backlinks for Faster Rankings](https://www.youtube.com/watch?v=qO5xeRYk20Q) `0:09:40`
	- [ ] [Get THOUSANDS of Backlinks On Semi-Autopilot](https://www.youtube.com/watch?v=SbDIDbiVPzs) `0:06:32`
	- [ ] [How To Get The Most Out Of Google Analytics](https://www.youtube.com/watch?v=a9yVBuk5Amo) `0:07:45`
	- [ ] [How to Setup Google Search Console](https://www.youtube.com/watch?v=7TKyZR5XQqQ) `0:09:21`
	- [ ] [How to Use Advanced Features in Google Analytics](https://www.youtube.com/watch?v=hAkiH_3hC9E) `0:10:52`
	- [ ] [A Deep Dive Into Branding, Data & Experience](https://www.youtube.com/watch?v=738TjOhTq3k) `0:14:03`
	- [ ] [How To Create A Compelling Brand](https://www.youtube.com/watch?v=ttT3IbkZ_Qo) `0:05:52`
	- [ ] [Designing Your Customer Experience & Case Studies](https://www.youtube.com/watch?v=CRz0pn3N5bs) `0:07:32`

### Be able to frame an ML problem
- [ ] [AWS: Types of Machine Learning Solutions](https://www.aws.training/learningobject/video?id=27224)
- [ ] [Article: Apply Machine Learning to your Business](https://theaisummer.com/Machine-Learning-Business/)
- [ ] [Article: Resilience and Vibrancy: The 2020 Data & AI Landscape](https://mattturck.com/data2020/)
- [ ] [Article: Software 2.0](https://medium.com/@karpathy/software-2-0-a64152b37c35)
- [ ] [Article: How Facebook uses super-efficient AI models to detect hate speech](https://ai.facebook.com/blog/how-facebook-uses-super-efficient-ai-models-to-detect-hate-speech)
- [ ] [Article: Highlights from ICML 2020](https://lewtun.github.io/blog/research/conference/2020/07/31/icml2020.html)
- [ ] [Article: Recent Advances in Google Translate](https://ai.googleblog.com/2020/06/recent-advances-in-google-translate.html)
- [ ] [Article: A Peek at Trends in Machine Learning](https://medium.com/@karpathy/a-peek-at-trends-in-machine-learning-ab8a1085a106)
- [ ] [Article: How to deliver on Machine Learning projects](https://mlpowered.com/posts/how-to-deliver-on-machine-learning-projects/)
- [ ] [Article: Data Science as a Product](https://blog.picnic.nl/data-science-as-a-product-f383dead5aa4)
- [ ] [Article: Customer service is full of machine learning problems](http://nlathia.github.io/2020/06/Customer-service-machine-learning.html)
- [ ] [Article: Choosing Problems in Data Science and Machine Learning](https://eugeneyan.com/writing/how-to-choose-problems/)
- [ ] [Article: Why finance is deploying natural language processing](https://mitsloan.mit.edu/ideas-made-to-matter/why-finance-deploying-natural-language-processing)
- [ ] [Article: Cannes: How ML saves us $1.7M a year on document previews](https://dropbox.tech/machine-learning/cannes--how-ml-saves-us--1-7m-a-year-on-document-previews)
- [ ] [Article: Machine Learning @ Monzo in 2020](http://nlathia.github.io/2020/10/Monzo-ML.html)
- [ ] [Book: AI Superpowers: China, Silicon Valley, and the New World Order](https://www.amazon.com/AI-Superpowers-China-Silicon-Valley/dp/132854639X)
- [ ] [Book: A Human's Guide to Machine Intelligence](https://www.amazon.com/Humans-Guide-Machine-Intelligence-Algorithms/dp/0525560882)
- [ ] [Book: The Future Computed](https://news.microsoft.com/uploads/2018/01/The-Future-Computed.pdf)
- [ ] [Book: Machine Learning Yearning by Andrew Ng](http://www.mlyearning.org/)
- [ ] [Book: Prediction Machines: The Simple Economics of Artificial Intelligence](https://www.amazon.com/Prediction-Machines-Economics-Artificial-Intelligence/dp/1633695670)
- [ ] [Book: Building Machine Learning Powered Applications: Going from Idea to Product](https://www.amazon.com/Building-Machine-Learning-Powered-Applications/dp/149204511X/)
- [ ] [Coursera: AI For Everyone](https://www.coursera.org/learn/ai-for-everyone)
- [ ] [Datacamp: Case Studies in Statistical Thinking](https://www.datacamp.com/courses/case-studies-in-statistical-thinking)
- [ ] [Datacamp: Data Science for Everyone](https://www.datacamp.com/courses/data-science-for-everyone)
- [ ] [Datacamp: Machine Learning with the Experts: School Budgets](https://www.datacamp.com/courses/machine-learning-with-the-experts-school-budgets)
- [ ] [Datacamp: Machine Learning for Everyone](https://www.datacamp.com/courses/machine-learning-for-everyone)
- [ ] [Datacamp: Analyzing Police Activity with pandas](https://www.datacamp.com/courses/analyzing-police-activity-with-pandas)
- [ ] [Datacamp: HR Analytics in Python: Predicting Employee Churn](https://www.datacamp.com/courses/hr-analytics-in-python-predicting-employee-churn)
- [ ] [Datacamp: Predicting Customer Churn in Python](https://www.datacamp.com/courses/predicting-customer-churn-in-python)
- [ ] [Datacamp: Data Science for Managers](https://www.datacamp.com/courses/data-science-for-managers)
- [ ] [Facebook: Field Guide to Machine Learning](https://research.fb.com/the-facebook-field-guide-to-machine-learning-video-series/)
- [ ] [Google: Introduction to Machine Learning Problem Framing](https://developers.google.com/machine-learning/problem-framing)
- [ ] [Microsoft: Define an AI strategy to create business value](https://aischool.microsoft.com/en-us/business/learning-paths/define-an-ai-strategy-to-create-business-value)
- [ ] [Microsoft: Discover ways to foster an AI-ready culture in your business](https://aischool.microsoft.com/en-us/business/learning-paths/discover-ways-to-foster-an-ai-ready-culture-in-your-business)
- [ ] [Microsoft: Identify guiding principles for responsible AI in your business](https://aischool.microsoft.com/en-us/business/learning-paths/identify-guiding-principles-for-responsible-ai-in-your-business)
- [ ] [Microsoft: Introduction to AI technology for business leaders](https://aischool.microsoft.com/en-us/business/learning-paths/introduction-to-ai-technology-for-business-leaders)
- [ ] [Pluralsight: How to Think About Machine Learning Algorithms](https://www.pluralsight.com/courses/machine-learning-algorithms)
- [ ] [State of AI Report 2020](https://docs.google.com/presentation/d/1ZUimafgXCBSLsgbacd6-a-dqO7yLyzIl1ZJbiCBUUT4/edit#slide=id.g557254d430_0_0)
- [ ] [Udacity: Problem Solving with Advanced Analytics](https://www.udacity.com/course/problem-solving-with-advanced-analytics--ud976)
- [ ] [Youtube: Vincent Warmerdam: The profession of solving (the wrong problem) | PyData Amsterdam 2019](https://youtu.be/kYMfE9u-lMo)
- [ ] [Youtube: Making Money from AI by Predicting Sales - Jay's Intro to AI Part 2](https://youtu.be/V4-lXSs3jrk)
- [ ] [Youtube: How does YouTube recommend videos? - AI EXPLAINED!](https://www.youtube.com/watch?v=wDxTWp3KMMs) `0:33:53`
- [ ] [Youtube: How does Google Translate's AI work?](https://www.youtube.com/watch?v=sIoHFPGOY0I) `0:15:02`
- [ ] [Youtube: Data Science in Finance](https://www.youtube.com/watch?v=mbOkL39Uabs) `0:17:52`
- [ ] [Youtube: The Age of AI](https://www.youtube.com/playlist?list=PLjq6DwYksrzz_fsWIpPcf6V7p2RNAneKc)
	- [ ] [How Far is Too Far? | The Age of A.I.](https://www.youtube.com/watch?v=UwsrzCVZAb8) `0:34:39`
	- [ ] [Healed through A.I. | The Age of A.I.](https://www.youtube.com/watch?v=V5aZjsWM2wo) `0:39:55`
	- [ ] [Using A.I. to build a better human | The Age of A.I.](https://www.youtube.com/watch?v=lrv8ga02VNg) `0:44:27`
	- [ ] [Love, art and stories: decoded | The Age of A.I.](https://www.youtube.com/watch?v=Kr1fmKVY3cA) `0:38:57`
	- [ ] [The 'Space Architects' of Mars | The Age of A.I.](https://www.youtube.com/watch?v=lIvrIKaNCRE) `0:30:10`
	- [ ] [Will a robot take my job? | The Age of A.I.](https://www.youtube.com/watch?v=f2aocKWrPG8) `0:36:14`
	- [ ] [Saving the world one algorithm at a time | The Age of A.I.](https://www.youtube.com/watch?v=0wy4u34fii4) `0:46:37`
	- [ ] [How A.I. is searching for Aliens | The Age of A.I.](https://www.youtube.com/watch?v=VwtC_4t2g5M) `0:36:12`
- [ ] [Youtube: Gradient Dissent Podcast](https://www.youtube.com/playlist?list=PLD80i8An1OEEb1jP0sjEyiLG8ULRXFob_)
	- [ ] [DeepChem creator Bharath Ramsundar on using deep learning for molecules and medicine discovery](https://www.youtube.com/watch?v=GnkpVjp117k) `0:55:11`
	- [ ] [ML Research and Production Pipelines with Chip Huyen](https://www.youtube.com/watch?v=6adNHwE5PHY) `0:43:07`
	- [ ] [Product Management for AI with Peter Skomoroch](https://www.youtube.com/watch?v=hSyb3xEvCrI) `1:28:14`
	- [ ] [Slow down and change one thing at a time - Advancing AI research with Josh Tobin](https://www.youtube.com/watch?v=G6AgmZ6_R3U) `0:48:19`
	- [ ] [Societal Impacts of Artificial Intelligence with Miles Brundage](https://www.youtube.com/watch?v=O2ya8M72y0U) `1:02:25`
	- [ ] [Deep Reinforcement Learning and Robotics with Peter Welinder](https://www.youtube.com/watch?v=1VI3xTh-TMA) `0:54:22`
	- [ ] [Machine learning across industries with Vicki Boykis](https://www.youtube.com/watch?v=pOnRSYSNuXI) `0:34:02`
	- [ ] [Designing ML models for millions of consumer robots - Angela Bassa and Danielle Dean](https://www.youtube.com/watch?v=W55uO4gIlQ4) `0:52:38`
	- [ ] [Building trustworthy AI systems and combating potential malicious use – A conversation w/ Jack Clark](https://www.youtube.com/watch?v=nv_f1Gk8Ybk) `0:55:56`
	- [ ] [Rachael Tatman - Conversational A.I. and Linguistics](https://www.youtube.com/watch?v=n_CTGZSq4m0) `0:36:51`
	- [ ] [Nicolas Koumchatzky - Machine Learning in Production for Self Driving Cars](https://www.youtube.com/watch?v=NbiG8ZuRsqU) `0:44:56`
	- [ ] [Brandon Rohrer - Machine Learning in Production for Robots](https://www.youtube.com/watch?v=_Ot35PspXw4) `0:34:31`
- [ ] [Youtube: Accuracy as a Failure](https://youtu.be/YGuVuZpTop0)
- [ ] [Youtube: Using Intent Data to Optimize the Self-Solve Experience](https://lucidworks.com/post/using-intent-data-to-optimize-the-self-solve-experience/)
- [ ] [Youtube: Trillions of Questions, No Easy Answers: A (home) movie about how Google Search works](https://www.youtube.com/watch?v=tFq6Q_muwG0)
- [ ] [Youtube: Hugging Face, Transformers | NLP Research and Open Source | Interview with Julien Chaumond](https://www.youtube.com/watch?v=ejWkDviM5QM)
- [ ] [Youtube: Vincent Warmerdam - Playing by the Rules-Based-Systems | PyData Eindhoven 2020](https://www.youtube.com/watch?v=nJAmN6gWdK8&ab_channel=PyData)
- [ ] [Youtube: Google Machine Learning System Design Mock Interview](https://www.youtube.com/watch?v=uF1V2MqX2U0)
- [ ] [Youtube: Netflix Machine Learning Mock Interview: Type-ahead Search](https://www.youtube.com/watch?v=12Ry6kGPQVs)
- [ ] [Youtube: Machine Learning design: Search engine for Q&A](https://www.youtube.com/watch?v=J_0ms1ayqKE)
- [ ] [Youtube: Engineering Systems for Real-Time Predictions @DoorDash](https://www.youtube.com/watch?v=sEZsIUBIhNk)

### Be familiar with data ethics
- [ ] [Article: How to Detect Bias in AI](https://www.maartengrootendorst.com/blog/bias/)
- [ ] [Practical Data Ethics](http://ethics.fast.ai/)
    - [ ] [Lesson 1: Disinformation](http://ethics.fast.ai/videos/?lesson=1)
    - [ ] [Lesson 2: Bias & Fairness](http://ethics.fast.ai/videos/?lesson=2)
    - [ ] [Lesson 3: Ethical Foundations & Practical Tools](http://ethics.fast.ai/videos/?lesson=3)
    - [ ] [Lesson 4: Privacy and surveillance](http://ethics.fast.ai/videos/?lesson=4)
    - [ ] [Lesson 4 continued: Privacy and surveillance](http://ethics.fast.ai/videos/?lesson=5)
    - [ ] [Lesson 5.1: The problem with metrics](http://ethics.fast.ai/videos/?lesson=6)
    - [ ] [Lesson 5.2: Our Ecosystem, Venture Capital, & Hypergrowth](http://ethics.fast.ai/videos/?lesson=7)
    - [ ] [Lesson 5.3: Losing the Forest for the Trees, guest lecture by Ali Alkhatib](http://ethics.fast.ai/videos/?lesson=8)
    - [ ] [Lesson 6: Algorithmic Colonialism, and Next Steps](http://ethics.fast.ai/videos/?lesson=9)

### Be able to import data from multiple sources
- [ ] [Docs: Beautiful Soup Documentation](https://www.crummy.com/software/BeautifulSoup/bs4/doc/)
- [ ] [Datacamp: Importing Data in Python (Part 2)](https://www.datacamp.com/courses/importing-data-in-python-part-2)
- [ ] [Datacamp: Web Scraping in Python](https://www.datacamp.com/courses/web-scraping-with-python)

### Be able to setup data annotation efficiently
- [ ] [Article: Create A Synthetic Image Dataset — The “What”, The “Why” and The “How”](https://towardsdatascience.com/create-a-synthetic-image-dataset-the-what-the-why-and-the-how-f820e6b6f718)
- [ ] [Article: We need Synthetic Data](https://towardsdatascience.com/we-need-synthetic-data-e6f90a8532a4)
- [ ] [Article: Weak Supervision for Online Discussions](https://trishalaneeraj.github.io/2020-07-05/weak-supervision)
- [ ] [Youtube: Snorkel: Dark Data and Machine Learning - Christopher Ré](https://www.youtube.com/watch?v=yu15Nf5eJEE)
- [ ] [Youtube: Training a NER Model with Prodigy and Transfer Learning](https://youtu.be/59BKHO_xBPA)
- [ ] [Youtube: Training a New Entity Type with Prodigy – annotation powered by active learning](https://youtu.be/l4scwf8KeIA)
- [ ] [Youtube: ECCV 2020 WSL tutorial: 4. Human-in-the-loop annotations](https://youtu.be/PCwpO3mLhZk?list=PLcD_yLvcdUll95mAnBDV0rZKhfClJMZMr)

### Be able to manipulate data with Numpy
- [ ] [Article: A Visual Intro to NumPy and Data Representation](https://jalammar.github.io/visual-numpy/)
- [ ] [Article: NumPy Illustrated: The Visual Guide to NumPy](https://medium.com/better-programming/numpy-illustrated-the-visual-guide-to-numpy-3b1d4976de1d)
- [ ] [Article: NumPy Fundamentals for Data Science and Machine Learning](https://pabloinsente.github.io/intro-numpy-fundamentals)
- [ ] [Datacamp: Intro to Python for Data Science](https://www.datacamp.com/courses/intro-to-python-for-data-science)
- [ ] [Pluralsight: Working with Multidimensional Data Using NumPy](https://www.pluralsight.com/courses/numpy-working-with-multidimensional-data)

### Be able to manipulate data with Pandas
- [ ] [Article: Visualizing Pandas' Pivoting and Reshaping Functions](https://jalammar.github.io/visualizing-pandas-pivoting-and-reshaping/)
- [ ] [Article: A Gentle Visual Intro to Data Analysis in Python Using Pandas](https://jalammar.github.io/gentle-visual-intro-to-data-analysis-python-pandas/)
- [ ] [Article: Comprehensive Guide to Grouping and Aggregating with Pandas](https://pbpython.com/groupby-agg.html)
- [ ] [Article: 8 Python Pandas Value_counts() tricks that make your work more efficient](https://re-thought.com/pandas-value_counts/)
- [ ] [Datacamp: pandas Foundations](https://www.datacamp.com/courses/pandas-foundations)
- [ ] [Datacamp: Pandas Joins for Spreadsheet Users](https://www.datacamp.com/courses/pandas-joins-for-spreadsheet-users)
- [ ] [Datacamp: Manipulating DataFrames with pandas](https://www.datacamp.com/courses/manipulating-dataframes-with-pandas)
- [ ] [Datacamp: Merging DataFrames with pandas](https://www.datacamp.com/courses/merging-dataframes-with-pandas)
- [ ] [Datacamp: Data Manipulation with pandas](https://www.datacamp.com/courses/data-manipulation-with-pandas)
- [ ] [Datacamp: Optimizing Python Code with pandas](https://www.datacamp.com/courses/optimizing-python-code-with-pandas)
- [ ] [Datacamp: Streamlined Data Ingestion with pandas](https://www.datacamp.com/courses/streamlined-data-ingestion-with-pandas)
- [ ] [Datacamp: Analyzing Marketing Campaigns with pandas](https://www.datacamp.com/courses/analyzing-marketing-campaigns-with-pandas)
- [ ] [edX: Implementing Predictive Analytics with Spark in Azure HDInsight](https://www.edx.org/course/implementing-predictive-analytics-spark-microsoft-dat202-3x-2)
- [ ] [Article: Modern Pandas](https://tomaugspurger.github.io)
    - [ ] [Modern Pandas (Part 1)](https://tomaugspurger.github.io/modern-1-intro.html)
    - [ ] [Modern Pandas (Part 2)](https://tomaugspurger.github.io/method-chaining.html)
    - [ ] [Modern Pandas (Part 3)](https://tomaugspurger.github.io/modern-3-indexes.html)
    - [ ] [Modern Pandas (Part 4)](https://tomaugspurger.github.io/modern-4-performance.html)
    - [ ] [Modern Pandas (Part 5)](https://tomaugspurger.github.io/modern-5-tidy.html)
    - [ ] [Modern Pandas (Part 6)](https://tomaugspurger.github.io/modern-6-visualization.html)
    - [ ] [Modern Pandas (Part 7)](https://tomaugspurger.github.io/modern-7-timeseries.html)
    - [ ] [Modern Pandas (Part 8)](https://tomaugspurger.github.io/modern-8-scaling.html)

### Be able to manipulate data in spreadsheets
- [ ] [Datacamp: Spreadsheet basics](https://www.datacamp.com/courses/spreadsheet-basics)
- [ ] [Datacamp: Data Analysis with Spreadsheets](https://www.datacamp.com/courses/data-analysis-with-spreadsheets)
- [ ] [Datacamp: Intermediate Spreadsheets for Data Science](https://www.datacamp.com/courses/intermediate-spreadsheets-for-data-science)
- [ ] [Datacamp: Pivot Tables with Spreadsheets](https://www.datacamp.com/courses/pivot-tables-with-spreadsheets)
- [ ] [Datacamp: Data Visualization in Spreadsheets](https://www.datacamp.com/courses/data-visualization-in-spreadsheets)
- [ ] [Datacamp: Introduction to Statistics in Spreadsheets](https://www.datacamp.com/courses/statistics-in-spreadsheets)
- [ ] [Datacamp: Conditional Formatting in Spreadsheets](https://www.datacamp.com/courses/conditional-formatting-in-spreadsheets)
- [ ] [Datacamp: Marketing Analytics in Spreadsheets](https://www.datacamp.com/courses/marketing-analytics-in-spreadsheets)
- [ ] [Datacamp: Error and Uncertainty in Spreadsheets](https://www.datacamp.com/courses/error-and-uncertainty-in-spreadsheets)
- [ ] [edX: Analyzing and Visualizing Data with Excel](https://www.edx.org/course/analyzing-visualizing-data-excel-microsoft-dat206x-7)

### Be able to manipulate data in databases
- [ ] [Codecademy: SQL Track](https://www.codecademy.com/courses/learn-sql)
- [ ] [Datacamp: Intro to SQL for Data Science](https://www.datacamp.com/courses/intro-to-sql-for-data-science)
- [ ] [Datacamp: Introduction to MongoDB in Python](https://www.datacamp.com/courses/introduction-to-using-mongodb-for-data-science-with-python)
- [ ] [Datacamp: Intermediate SQL](https://www.datacamp.com/courses/intermediate-sql)
- [ ] [Datacamp: Exploratory Data Analysis in SQL](https://www.datacamp.com/courses/sql-for-exploratory-data-analysis)
- [ ] [Datacamp: Joining Data in PostgreSQL](https://www.datacamp.com/courses/joining-data-in-postgresql)
- [ ] [Datacamp: Querying with TransactSQL](https://www.datacamp.com/courses/querying-with-transact-sql)
- [ ] [Datacamp: Introduction to Databases in Python](https://www.datacamp.com/courses/introduction-to-relational-databases-in-python)
- [ ] [Datacamp: Reporting in SQL](https://www.datacamp.com/courses/reporting-in-sql)
- [ ] [Datacamp: Applying SQL to Real-World Problems](https://www.datacamp.com/courses/applying-sql-to-real-world-problems)
- [ ] [Datacamp: Analyzing Business Data in SQL](https://www.datacamp.com/courses/analyzing-business-data-in-sql)
- [ ] [Datacamp: Data-Driven Decision Making in SQL](https://www.datacamp.com/courses/data-driven-decision-making-with-sql)
- [ ] [Datacamp: Database Design](https://www.datacamp.com/courses/database-design)
- [ ] [Udacity: SQL for Data Analysis](https://www.udacity.com/course/sql-for-data-analysis--ud198)
- [ ] [Udacity: Intro to relational database](https://www.udacity.com/course/intro-to-relational-databases--ud197)
- [ ] [Udacity: Database Systems Concepts & Design](https://www.udacity.com/course/database-systems-concepts-design--ud150)

### Be able to use Linux tools
- [ ] [Article: Streamline your projects using Makefile](https://dev.to/yankee/streamline-projects-using-makefile-28fe)
- [ ] [Calmcode: makefiles](https://calmcode.io/makefiles/the-problem.html)
- [ ] [Calmcode: entr](https://calmcode.io/entr/introduction.html)
- [ ] [Codecademy: Learn the Command Line](https://www.codecademy.com/learn/learn-the-command-line)
- [ ] [Datacamp: Introduction to Shell for Data Science](https://www.datacamp.com/courses/introduction-to-shell-for-data-science)
- [ ] [Datacamp: Introduction to Bash Scripting](https://www.datacamp.com/courses/introduction-to-bash-scripting)
- [ ] [Datacamp: Data Processing in Shell](https://www.datacamp.com/courses/data-processing-in-shell)
- [ ] [LaunchSchool: Introduction to Commandline](https://launchschool.com/books/command_line)
- [ ] [Learn Enough Command Line to be dangerous](http://www.learnenough.com/command-line-tutorial)
- [ ] [Thoughtbot: Mastering the Shell](https://thoughtbot.com/upcase/mastering-the-shell)
- [ ] [Thoughtbot: tmux](https://thoughtbot.com/upcase/tmux)
- [ ] [Udacity: Linux Command Line Basics](https://www.udacity.com/course/linux-command-line-basics--ud595)
- [ ] [Udacity: Shell Workshop](https://www.udacity.com/course/shell-workshop--ud206)
- [ ] [Web Bos:  Command Line Power User](https://www.youtube.com/watch?v=DP218aBHm1Q&list=PLu8EoSxDXHP7tXPJp5ZmUpuT7sFvrswzf&index=2)
- [ ] [Youtube: GNU Parallel](https://www.youtube.com/playlist?list=PL284C9FF2488BC6D1)

### Be able to perform feature engineering
- [ ] [Article: Tips for Advanced Feature Engineering](https://www.maartengrootendorst.com/blog/feature-engineering/)
- [ ] [Article: Preparing data for a machine learning model](https://www.jeremyjordan.me/preparing-data-for-a-machine-learning-model/)
- [ ] [Article: Feature selection for a machine learning model](https://www.jeremyjordan.me/feature-selection/)
- [ ] [Article: Learning from imbalanced data](https://www.jeremyjordan.me/imbalanced-data/)
- [ ] [Article: Hacker's Guide to Data Preparation for Machine Learning](https://www.curiousily.com/posts/hackers-guide-to-data-preparation-for-machine-learning/)
- [ ] [Article: Practical Guide to Handling Imbalanced Datasets](https://www.curiousily.com/posts/practical-guide-to-handling-imbalanced-datasets/)
- [ ] [Datacamp: Analyzing Social Media Data in Python](https://www.datacamp.com/courses/analyzing-social-media-data-in-python)
- [ ] [Datacamp: Dimensionality Reduction in Python](https://www.datacamp.com/courses/dimensionality-reduction-in-python)
- [ ] [Datacamp: Preprocessing for Machine Learning in Python](https://www.datacamp.com/courses/preprocessing-for-machine-learning-in-python)
- [ ] [Datacamp: Data Types for Data Science](https://www.datacamp.com/courses/data-types-for-data-science)
- [ ] [Datacamp: Cleaning Data in Python](https://www.datacamp.com/courses/cleaning-data-in-python)
- [ ] [Datacamp: Feature Engineering for Machine Learning in Python](https://www.datacamp.com/courses/feature-engineering-for-machine-learning-in-python)
- [ ] [Datacamp: Importing & Managing Financial Data in Python](https://www.datacamp.com/courses/importing-managing-financial-data-in-python)
- [ ] [Datacamp: Manipulating Time Series Data in Python](https://www.datacamp.com/courses/manipulating-time-series-data-in-python)
- [ ] [Datacamp: Working with Geospatial Data in Python](https://www.datacamp.com/courses/working-with-geospatial-data-in-python)
- [ ] [Datacamp: Analyzing IoT Data in Python](https://www.datacamp.com/courses/analyzing-iot-data-in-python)
- [ ] [Datacamp: Dealing with Missing Data in Python](https://www.datacamp.com/courses/dealing-with-missing-data-in-python)
- [ ] [Datacamp: Exploratory Data Analysis in Python](https://www.datacamp.com/courses/exploratory-data-analysis-in-python)
- [ ] [edX: Data Science Essentials](https://www.edx.org/course/data-science-essentials-microsoft-dat203-1x-5)
- [ ] [Google: Feature Engineering](https://www.coursera.org/learn/feature-engineering)
- [ ] [Udacity: Creating an Analytical Dataset](https://www.udacity.com/course/creating-an-analytical-dataset--ud977)

### Be able to experiment in a notebook
- [ ] [Article: Securely storing configuration credentials in a Jupyter Notebook](http://veekaybee.github.io/2020/02/25/secrets/)
- [ ] [Article: Automatically Reload Modules with %autoreload](https://switowski.com/blog/ipython-autoreload)
- [ ] [Calmcode: ipywidgets](https://calmcode.io/ipywidgets/introduction.html)
- [ ] [Documentation: Jupyter Lab](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html)
- [ ] [Pluralsight: Getting Started with Jupyter Notebook and Python](https://www.pluralsight.com/courses/jupyter-notebook-python)
- [ ] [Youtube: William Horton - A Brief History of Jupyter Notebooks](https://www.youtube.com/watch?v=kFhhCOeYcGw)
- [ ] [Youtube: I Like Notebooks](https://www.youtube.com/watch?v=9Q6sLbz37gk)
- [ ] [Youtube: I don't like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)](https://www.youtube.com/watch?v=7jiPeIFXb6U)
- [ ] [Youtube: Ryan Herr - After model.fit, before you deploy| JupyterCon 2020](https://www.youtube.com/watch?v=hGHwu1h3l6g)
- [ ] [Youtube: nbdev live coding with Hamel Husain](https://www.youtube.com/watch?v=ZJTop5uqC2U&feature=youtu.be)
- [ ] [Youtube: How to Use JupyterLab](https://www.youtube.com/watch?v=A5YyoCKxEOU&feature=emb_logo)

### Be able to visualize data
- [ ] [Article: Creating a Catchier Word Cloud Presentation](https://towardsdatascience.com/creating-a-catchier-word-cloud-presentation-e17fa2c1ef46)
- [ ] [Article: Effectively Using Matplotlib](https://pbpython.com/effective-matplotlib.html)
- [ ] [Datacamp: Introduction to Data Visualization with Python](https://www.datacamp.com/courses/introduction-to-data-visualization-with-python)
- [ ] [Datacamp: Introduction to Seaborn](https://www.datacamp.com/courses/introduction-to-seaborn)
- [ ] [Datacamp: Introduction to Matplotlib](https://www.datacamp.com/courses/introduction-to-matplotlib)
- [ ] [Datacamp: Intermediate Data Visualization with Seaborn](https://www.datacamp.com/courses/data-visualization-with-seaborn)
- [ ] [Datacamp: Visualizing Time Series Data in Python](https://www.datacamp.com/courses/visualizing-time-series-data-in-python)
- [ ] [Datacamp: Improving Your Data Visualizations in Python](https://www.datacamp.com/courses/improving-your-data-visualizations-in-python)
- [ ] [Datacamp: Visualizing Geospatial Data in Python](https://www.datacamp.com/courses/visualizing-geospatial-data-in-python)
- [ ] [Datacamp: Interactive Data Visualization with Bokeh](https://www.datacamp.com/courses/interactive-data-visualization-with-bokeh)
- [ ] [Udacity: Data Visualization in Tableau](https://www.udacity.com/course/data-visualization-in-tableau--ud1006)
- [ ] [Youtube: Jake VanderPlas - Exploratory Data Visualization with Vega, Vega-Lite, and Altair - PyCon 2018](https://www.youtube.com/watch?v=ms29ZPUKxbU)
- [ ] [Youtube: Shantam Raj- Rapidly emulating professional visualizations from New York Times| PyData Global 2020](https://www.youtube.com/watch?v=pPHhv7qsQ_8)
- [ ] [UWData: Data Visualization Curriculum](https://github.com/uwdata/visualization-curriculum)

### Be able to do literature review using research papers
- [ ] [Paper: A Neural Probabilistic Language Model](http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf)
- [ ] [Paper: Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/pdf/1301.3781.pdf)
- [ ] [Paper: Sequence to Sequence Learning with Neural Networks](https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf)
- [ ] [Paper: Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473)
- [ ] [Paper: Attention Is All You Need](https://arxiv.org/abs/1706.03762)
- [ ] [Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)
- [ ] [Paper: XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237)
- [ ] [Paper: Synonyms Based Term Weighting Scheme: An Extension to TF.IDF](https://www.researchgate.net/publication/306362767_Synonyms_Based_Term_Weighting_Scheme_An_Extension_to_TFIDF)
- [ ] [Paper: RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692)
- [ ] [Paper: GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://www.nyu.edu/projects/bowman/glue.pdf)
- [ ] [Paper: Amazon.com Recommendations Item-to-Item Collaborative Filtering](https://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf)
- [ ] [Paper: Collaborative Filtering for Implicit Feedback Datasets](http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=34AEEE06F0C2428083376C26C71D7CFF?doi=10.1.1.167.5120&rep=rep1&type=pdf)
- [ ] [Paper: BPR: Bayesian Personalized Ranking from Implicit Feedback](https://arxiv.org/pdf/1205.2618.pdf)
- [ ] [Paper: Factorization Machines](https://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/Rendle2010FM.pdf)
- [ ] [Paper: Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf)
- [ ] [Paper: Neural Factorization Machines for Sparse Predictive Analytics](https://l.facebook.com/l.php?u=https%3A%2F%2Farxiv.org%2Fpdf%2F1708.05027.pdf&h=AT3VuDk1rSqAkgo1x79wl9FXtb7SFMT01B1MXLMvp0O8syX2BuHYB70EJkMwVngQtShj0yTTn6laoRQ3I7StkJQJ9j1b8DiHM7gXNv7dWvL9S_khSF4wWZA9No70BhewiggJ8a8Pa0jTnq4_ppOIsk-qDYVkyJM5QuoSSg)
- [ ] [Paper: Multiword Expressions: A Pain in the Neck for NLP](http://lingo.stanford.edu/pubs/WP-2001-03.pdf)
- [ ] [Paper: PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://arxiv.org/pdf/1912.01703.pdf)
- [ ] [Paper: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS](https://arxiv.org/pdf/1909.11942.pdf)
- [ ] [Paper: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://arxiv.org/abs/1902.06162)
- [ ] [Paper: A Simple Framework for Contrastive Learning of Visual Representations](https://arxiv.org/pdf/2002.05709.pdf)
- [ ] [Paper: Self-Supervised Learning of Pretext-Invariant Representations](https://arxiv.org/abs/1912.01991)
- [ ] [Paper: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://arxiv.org/abs/2001.07685)
- [ ] [Paper: Self-Labelling via Simultalaneous Clustering and Representation Learning](https://www.robots.ox.ac.uk/~vgg/research/self-label/)
- [ ] [Paper: A Survey on Contextual Embeddings](https://arxiv.org/abs/2003.07278v1)
- [ ] [Paper: A survey on Semi-, Self- and Unsupervised Techniques in Image Classification](https://arxiv.org/abs/2002.08721)
- [ ] [Paper: Shortcut Learning in Deep Neural Networks](https://arxiv.org/abs/2004.07780)
- [ ] [Paper: Multi-document Summarization by using TextRank and Maximal Marginal Relevance for Text in Bahasa Indonesia](https://www.researchgate.net/publication/338940065_Multi-document_Summarization_by_using_TextRank_and_Maximal_Marginal_Relevance_for_Text_in_Bahasa_Indonesia)
- [ ] [Paper: Train Once, Test Anywhere: Zero-Shot Learning for Text Classification](https://arxiv.org/abs/1712.05972)
- [ ] [Paper: Zero-shot Text Classification With Generative Language Models](https://arxiv.org/abs/1912.10165)
- [ ] [Paper: How to Fine-Tune BERT for Text Classification?](https://arxiv.org/abs/1905.05583)
- [ ] [Paper: Universal Sentence Encoder](https://arxiv.org/abs/1803.11175)
- [ ] [Paper: Enriching Word Vectors with Subword Information](https://arxiv.org/abs/1607.04606)
- [ ] [Paper: Deep Learning Based Text Classification: A Comprehensive Review](https://arxiv.org/abs/2004.03705)
- [ ] [Paper: Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://arxiv.org/abs/2005.04118)
- [ ] [Paper: Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks](http://deeplearning.net/wp-content/uploads/2013/03/pseudo_label_final.pdf)
- [ ] [Paper: Temporal Ensembling for Semi-Supervised Learning](https://arxiv.org/abs/1610.02242)
- [ ] [Paper: Boosting Self-Supervised Learning via Knowledge Transfer](https://arxiv.org/abs/1805.00385)
- [ ] [Paper: Follow-up Question Generation](https://essay.utwente.nl/79491/1/Mandasari_MA_EEMCS.pdf)
- [ ] [Paper: The Hardware Lottery](https://arxiv.org/abs/2009.06489)
- [ ] [Paper: Question Generation via Overgenerating Transformations and Ranking](http://www.cs.cmu.edu/~mheilman/papers/heilman-smith-qg-tech-report.pdf)
- [ ] [Paper: Good Question! Statistical Ranking for Question Generation](https://www.aclweb.org/anthology/N10-1086.pdf)
- [ ] [Paper: Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)](https://arxiv.org/pdf/2010.02013v2.pdf)
- [ ] [Paper: Interpolation Consistency Training for Semi-Supervised Learning](https://arxiv.org/pdf/1903.03825.pdf)
- [ ] [Paper: Neural Text Generation: A Practical Guide](https://cs.stanford.edu/~zxie/textgen.pdf)
- [ ] [Paper: Pest Management In Cotton Farms: An AI-System Case Study from the Global South](https://dl.acm.org/doi/10.1145/3394486.3403363)
- [ ] [Paper: BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search](https://arxiv.org/abs/2010.10442)
- [ ] [Paper: On the surprising similarities between supervised and self-supervised models](https://arxiv.org/abs/2010.08377)
- [ ] [Paper: All-but-the-Top: Simple and Effective Postprocessing for Word Representations](https://arxiv.org/abs/1702.01417)
- [ ] [Paper: Simple and Effective Dimensionality Reduction for Word Embeddings](https://arxiv.org/abs/1708.03629)
- [ ] [Paper: A Survey on Visual Transformer](https://arxiv.org/abs/2012.12556v1)
- [ ] [Youtube: mixup: Beyond Empirical Risk Minimization (Paper Explained)](https://www.youtube.com/watch?v=a-VQfQqIMrE)

### Be able to model problems mathematically
- [ ] [3Blue1Brown: Essence of Calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr)
	- [ ] [The Essence of Calculus, Chapter 1](https://www.youtube.com/watch?v=WUvTyaaNkzM) `0:17:04`
	- [ ] [The paradox of the derivative | Essence of calculus, chapter 2](https://www.youtube.com/watch?v=9vKqVkMQHKk) `0:17:57`
	- [ ] [Derivative formulas through geometry | Essence of calculus, chapter 3](https://www.youtube.com/watch?v=S0_qX4VJhMQ) `0:18:43`
	- [ ] [Visualizing the chain rule and product rule | Essence of calculus, chapter 4](https://www.youtube.com/watch?v=YG15m2VwSjA) `0:16:52`
	- [ ] [What's so special about Euler's number e? | Essence of calculus, chapter 5](https://www.youtube.com/watch?v=m2MIpDrF7Es) `0:13:50`
	- [ ] [Implicit differentiation, what's going on here? | Essence of calculus, chapter 6](https://www.youtube.com/watch?v=qb40J4N1fa4) `0:15:33`
	- [ ] [Limits, L'Hôpital's rule, and epsilon delta definitions | Essence of calculus, chapter 7](https://www.youtube.com/watch?v=kfF40MiS7zA) `0:18:26`
	- [ ] [Integration and the fundamental theorem of calculus | Essence of calculus, chapter 8](https://www.youtube.com/watch?v=rfG8ce4nNh0) `0:20:46`
	- [ ] [What does area have to do with slope? | Essence of calculus, chapter 9](https://www.youtube.com/watch?v=FnJqaIESC2s) `0:12:39`
	- [ ] [Higher order derivatives | Essence of calculus, chapter 10](https://www.youtube.com/watch?v=BLkz5LGWihw) `0:05:38`
	- [ ] [Taylor series | Essence of calculus, chapter 11](https://www.youtube.com/watch?v=3d6DsjIBzJ4) `0:22:19`
	- [ ] [What they won't teach you in calculus](https://www.youtube.com/watch?v=CfW845LNObM) `0:16:22`
- [ ] [3Blue1Brown: Essence of linear algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
	- [ ] [Vectors, what even are they? | Essence of linear algebra, chapter 1](https://www.youtube.com/watch?v=fNk_zzaMoSs) `0:09:52`
	- [ ] [Linear combinations, span, and basis vectors | Essence of linear algebra, chapter 2](https://www.youtube.com/watch?v=k7RM-ot2NWY) `0:09:59`
	- [ ] [Linear transformations and matrices | Essence of linear algebra, chapter 3](https://www.youtube.com/watch?v=kYB8IZa5AuE) `0:10:58`
	- [ ] [Matrix multiplication as composition | Essence of linear algebra, chapter 4](https://www.youtube.com/watch?v=XkY2DOUCWMU) `0:10:03`
	- [ ] [Three-dimensional linear transformations | Essence of linear algebra, chapter 5](https://www.youtube.com/watch?v=rHLEWRxRGiM) `0:04:46`
	- [ ] [The determinant | Essence of linear algebra, chapter 6](https://www.youtube.com/watch?v=Ip3X9LOh2dk) `0:10:03`
	- [ ] [Inverse matrices, column space and null space | Essence of linear algebra, chapter 7](https://www.youtube.com/watch?v=uQhTuRlWMxw) `0:12:08`
	- [ ] [Nonsquare matrices as transformations between dimensions | Essence of linear algebra, chapter 8](https://www.youtube.com/watch?v=v8VSDg_WQlA) `0:04:27`
	- [ ] [Dot products and duality | Essence of linear algebra, chapter 9](https://www.youtube.com/watch?v=LyGKycYT2v0) `0:14:11`
	- [ ] [Cross products | Essence of linear algebra, Chapter 10](https://www.youtube.com/watch?v=eu6i7WJeinw) `0:08:53`
	- [ ] [Cross products in the light of linear transformations | Essence of linear algebra chapter 11](https://www.youtube.com/watch?v=BaM7OCEm3G0) `0:13:10`
	- [ ] [Cramer's rule, explained geometrically | Essence of linear algebra, chapter 12](https://www.youtube.com/watch?v=jBsC34PxzoM) `0:12:12`
	- [ ] [Change of basis | Essence of linear algebra, chapter 13](https://www.youtube.com/watch?v=P2LTAUO1TdA) `0:12:50`
	- [ ] [Eigenvectors and eigenvalues | Essence of linear algebra, chapter 14](https://www.youtube.com/watch?v=PFDu9oVAE-g) `0:17:15`
	- [ ] [Abstract vector spaces | Essence of linear algebra, chapter 15](https://www.youtube.com/watch?v=TgKwz5Ikpc8) `0:16:46`
- [ ] [3Blue1Brown: Neural networks](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
	- [ ] [But what is a Neural Network? | Deep learning, chapter 1](https://www.youtube.com/watch?v=aircAruvnKk) `0:19:13`
	- [ ] [Gradient descent, how neural networks learn | Deep learning, chapter 2](https://www.youtube.com/watch?v=IHZwWFHWa-w) `0:21:01`
	- [ ] [What is backpropagation really doing? | Deep learning, chapter 3](https://www.youtube.com/watch?v=Ilg3gGewQ5U) `0:13:54`
	- [ ] [Backpropagation calculus | Deep learning, chapter 4](https://www.youtube.com/watch?v=tIeHLnjs5U8) `0:10:17`
- [ ] [Article: A Visual Tour of Backpropagation](https://blog.jinay.dev/posts/backprop/)
- [ ] [Article: Entropy, Cross Entropy, and KL Divergence](https://towardsdatascience.com/entropy-cross-entropy-and-kl-divergence-17138ffab87b)
- [ ] [Article: Introduction to Linear Algebra for Applied Machine Learning with Python](https://pabloinsente.github.io/intro-linear-algebra)
- [ ] [Article: Entropy of a probability distribution — in layman’s terms](https://medium.com/@ajitrajasekharan/entropy-of-a-probability-distribution-explained-in-laymans-terms-8a4139b329bc)
- [ ] [Article: KL Divergence — in layman’s terms](https://medium.com/@ajitrajasekharan/what-is-kl-divergence-d1fb7921ee5b)
- [ ] [Article: Probability Distributions](https://www.simonwardjones.co.uk/posts/probability_distributions/)
- [ ] [Article: Relearning Matrices as Linear Functions](https://www.dhruvonmath.com/2018/12/31/matrices/)
- [ ] [Article: You Could Have Come Up With Eigenvectors - Here's How](https://www.dhruvonmath.com/2019/02/25/eigenvectors/)
- [ ] [Article: PageRank - How Eigenvectors Power the Algorithm Behind Google Search](https://www.dhruvonmath.com/2019/03/20/pagerank/)
- [ ] [Article: Interactive Visualization of Why Eigenvectors Matter](https://www.dhruvonmath.com/2020/07/26/who-cares-about-eigenvectors/)
- [ ] [Article: Cross-Entropy and KL Divergence](https://medium.com/swlh/cross-entropy-and-kl-divergence-522d9f71bd3d)
- [ ] [Article: Why Randomness Is Information?](https://medium.com/swlh/why-randomness-is-information-f2468966b29d)
- [ ] [Article: Basic Probability Theory](https://medium.com/swlh/probability-for-machine-learning-and-data-science-cccd4f4f1df1)
- [ ] [Book: Basics of Linear Algebra for Machine Learning](https://machinelearningmastery.com/linear_algebra_for_machine_learning/)
- [ ] [Datacamp: Introduction to Statistics in Python](https://www.datacamp.com/courses/introduction-to-statistics-in-python)
- [ ] [Datacamp: Foundations of Probability in Python](https://www.datacamp.com/courses/foundations-of-probability-in-python)
- [ ] [Datacamp: Statistical Thinking in Python (Part 1)](https://www.datacamp.com/courses/statistical-thinking-in-python-part-1)
- [ ] [Datacamp: Statistical Thinking in Python (Part 2)](https://www.datacamp.com/courses/statistical-thinking-in-python-part-2)
- [ ] [Datacamp: Statistical Simulation in Python](https://www.datacamp.com/courses/statistical-simulation-in-python)
- [ ] [edX: Essential Statistics for Data Analysis using Excel](https://www.edx.org/course/essential-statistics-data-analysis-using-microsoft-dat222x-1)
- [ ] [Computational Linear Algebra for Coders](https://github.com/fastai/numerical-linear-algebra)
- [ ] [Khan Academy: Precalculus](https://www.khanacademy.org/math/precalculus)
- [ ] [Khan Academy: Probability](https://www.khanacademy.org/mission/probability)
- [ ] [Khan Academy: Differential Calculus](https://www.khanacademy.org/mission/differential-calculus)
- [ ] [Khan Academy: Multivariable Calculus](https://www.khanacademy.org/math/multivariable-calculus)
- [ ] [Khan Academy: Linear Algebra](https://www.khanacademy.org/math/linear-algebra)
- [ ] [MIT: 18.06 Linear Algebra (Professor Strang)](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)
	- [ ] [1. The Geometry of Linear Equations](https://www.youtube.com/watch?v=J7DzL2_Na80) `0:39:49`
	- [ ] [2. Elimination with Matrices.](https://www.youtube.com/watch?v=QVKj3LADCnA) `0:47:41`
	- [ ] [3. Multiplication and Inverse Matrices](https://www.youtube.com/watch?v=FX4C-JpTFgY) `0:46:48`
	- [ ] [4. Factorization into A = LU](https://www.youtube.com/watch?v=MsIvs_6vC38) `0:48:05`
	- [ ] [5. Transposes, Permutations, Spaces R^n](https://www.youtube.com/watch?v=JibVXBElKL0) `0:47:41`
	- [ ] [6. Column Space and Nullspace](https://www.youtube.com/watch?v=8o5Cmfpeo6g) `0:46:01`
	- [ ] [9. Independence, Basis, and Dimension](https://www.youtube.com/watch?v=yjBerM5jWsc) `0:50:14`
	- [ ] [10. The Four Fundamental Subspaces](https://www.youtube.com/watch?v=nHlE7EgJFds) `0:49:20`
	- [ ] [11. Matrix Spaces; Rank 1; Small World Graphs](https://www.youtube.com/watch?v=2IdtqGM6KWU) `0:45:55`
	- [ ] [14. Orthogonal Vectors and Subspaces](https://www.youtube.com/watch?v=YzZUIYRCE38) `0:49:47`
	- [ ] [15. Projections onto Subspaces](https://www.youtube.com/watch?v=Y_Ac6KiQ1t0) `0:48:51`
	- [ ] [16. Projection Matrices and Least Squares](https://www.youtube.com/watch?v=osh80YCg_GM) `0:48:05`
	- [ ] [17. Orthogonal Matrices and Gram-Schmidt](https://www.youtube.com/watch?v=0MtwqhIwdrI) `0:49:09`
	- [ ] [21. Eigenvalues and Eigenvectors](https://www.youtube.com/watch?v=cdZnhQjJu4I) `0:51:22`
	- [ ] [22. Diagonalization and Powers of A](https://www.youtube.com/watch?v=13r9QY6cmjc) `0:51:50`
	- [ ] [24. Markov Matrices; Fourier Series](https://www.youtube.com/watch?v=lGGDIGizcQ0) `0:51:11`
	- [ ] [25. Symmetric Matrices and Positive Definiteness](https://www.youtube.com/watch?v=UCc9q_cAhho) `0:43:52`
	- [ ] [27. Positive Definite Matrices and Minima](https://www.youtube.com/watch?v=vF7eyJ2g3kU) `0:50:40`
	- [ ] [29. Singular Value Decomposition](https://www.youtube.com/watch?v=TX_vooSnhm8) `0:40:28`
	- [ ] [30. Linear Transformations and Their Matrices](https://www.youtube.com/watch?v=Ts3o2I8_Mxc) `0:49:27`
	- [ ] [31. Change of Basis; Image Compression](https://www.youtube.com/watch?v=0h43aV4aH7I) `0:50:13`
	- [ ] [33. Left and Right Inverses; Pseudoinverse](https://www.youtube.com/watch?v=Go2aLo7ZOlU) `0:41:52`
- [ ] [StatQuest: Statistics Fundamentals](https://www.youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9)
	- [ ] [StatQuest: Histograms, Clearly Explained](https://www.youtube.com/watch?v=qBigTkBLU6g) `0:03:42`
	- [ ] [StatQuest:  What is a statistical distribution?](https://www.youtube.com/watch?v=oI3hZJqXJuc) `0:05:14`
	- [ ] [StatQuest: The Normal Distribution, Clearly Explained!!!](https://www.youtube.com/watch?v=rzFX5NWojp0) `0:05:12`
	- [ ] [Statistics Fundamentals: Population Parameters](https://www.youtube.com/watch?v=vikkiwjQqfU) `0:14:31`
	- [ ] [Statistics Fundamentals: The Mean, Variance and Standard Deviation](https://www.youtube.com/watch?v=SzZ6GpcfoQY) `0:14:22`
	- [ ] [StatQuest: What is a statistical model?](https://www.youtube.com/watch?v=yQhTtdq_y9M) `0:03:45`
	- [ ] [StatQuest: Sampling A Distribution](https://www.youtube.com/watch?v=XLCWeSVzHUU) `0:03:48`
	- [ ] [Hypothesis Testing and The Null Hypothesis](https://www.youtube.com/watch?v=0oc49DyA3hU) `0:14:40`
	- [ ] [Alternative Hypotheses: Main Ideas!!!](https://www.youtube.com/watch?v=5koKb5B_YWo) `0:09:49`
	- [ ] [p-values: What they are and how to interpret them](https://www.youtube.com/watch?v=vemZtEM63GY) `0:11:22`
	- [ ] [How to calculate p-values](https://www.youtube.com/watch?v=JQc3yx0-Q9E) `0:25:15`
	- [ ] [p-hacking: What it is and how to avoid it!](https://www.youtube.com/watch?v=HDCOUXE3HMM) `0:13:44`
	- [ ] [Statistical Power, Clearly Explained!!!](https://www.youtube.com/watch?v=Rsc5znwR5FA) `0:08:19`
	- [ ] [Power Analysis, Clearly Explained!!!](https://www.youtube.com/watch?v=VX_M3tIyiYk) `0:16:44`
	- [ ] [Covariance and Correlation Part 1: Covariance](https://www.youtube.com/watch?v=qtaqvPAeEJY) `0:22:23`
	- [ ] [Covariance and Correlation Part 2: Pearson's Correlation](https://www.youtube.com/watch?v=xZ_z8KWkhXE) `0:19:13`
	- [ ] [StatQuest: R-squared explained](https://www.youtube.com/watch?v=2AQKmw14mHM) `0:11:01`
	- [ ] [The Central Limit Theorem](https://www.youtube.com/watch?v=YAlJCEDH2uY) `0:07:35`
	- [ ] [StatQuickie: Standard Deviation vs Standard Error](https://www.youtube.com/watch?v=A82brFpdr9g) `0:02:52`
	- [ ] [StatQuest: The standard error](https://www.youtube.com/watch?v=XNgt7F6FqDU) `0:11:43`
	- [ ] [StatQuest: Technical and Biological Replicates](https://www.youtube.com/watch?v=Exk0OoRG0PQ) `0:05:27`
	- [ ] [StatQuest - Sample Size and Effective Sample Size, Clearly Explained](https://www.youtube.com/watch?v=67zCIqdeXpo) `0:06:32`
	- [ ] [Bar Charts Are Better than Pie Charts](https://www.youtube.com/watch?v=RiEZ_hEf96A) `0:01:45`
	- [ ] [StatQuest: Boxplots, Clearly Explained](https://www.youtube.com/watch?v=fHLhBnmwUM0) `0:02:33`
	- [ ] [StatQuest: Logs (logarithms), clearly explained](https://www.youtube.com/watch?v=VSi0Z04fWj0) `0:15:37`
	- [ ] [StatQuest: Confidence Intervals](https://www.youtube.com/watch?v=TqOeMYtOc1w) `0:06:41`
	- [ ] [StatQuickie: Thresholds for Significance](https://www.youtube.com/watch?v=KEofcJ1tfkI) `0:06:40`
	- [ ] [StatQuickie: Which t test to use](https://www.youtube.com/watch?v=nnBJeb_I-q8) `0:05:10`
	- [ ] [StatQuest:  One or Two Tailed P-Values](https://www.youtube.com/watch?v=bsZGt-caXO4) `0:07:05`
	- [ ] [The Binomial Distribution and Test, Clearly Explained!!!](https://www.youtube.com/watch?v=J8jNoF-K8E8) `0:15:46`
	- [ ] [StatQuest: Quantiles and Percentiles, Clearly Explained!!!](https://www.youtube.com/watch?v=IFKQLDmRK0Y) `0:06:30`
	- [ ] [StatQuest: Quantile-Quantile Plots (QQ plots), Clearly Explained](https://www.youtube.com/watch?v=okjYjClSjOg) `0:06:55`
	- [ ] [StatQuest: Quantile Normalization](https://www.youtube.com/watch?v=ecjN6Xpv6SE) `0:04:51`
	- [ ] [StatQuest: Probability vs Likelihood](https://www.youtube.com/watch?v=pYxNSUDSFH4) `0:05:01`
	- [ ] [StatQuest: Maximum Likelihood, clearly explained!!!](https://www.youtube.com/watch?v=XepXtl9YKwc) `0:06:12`
	- [ ] [Maximum Likelihood for the Exponential Distribution, Clearly Explained! V2.0](https://www.youtube.com/watch?v=p3T-_LMrvBc) `0:09:39`
	- [ ] [Why Dividing By N Underestimates the Variance](https://www.youtube.com/watch?v=sHRBg6BhKjI) `0:17:14`
	- [ ] [Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!](https://www.youtube.com/watch?v=4KKV9yZCoM4) `0:11:24`
	- [ ] [Maximum Likelihood For the Normal Distribution, step-by-step!](https://www.youtube.com/watch?v=Dn6b9fCIUpM) `0:19:50`
	- [ ] [StatQuest: Odds and Log(Odds), Clearly Explained!!!](https://www.youtube.com/watch?v=ARfXDSkQf1Y) `0:11:30`
	- [ ] [StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!!](https://www.youtube.com/watch?v=8nm0G-1uJzA) `0:16:20`
	- [ ] [Live 2020-04-20!!! Expected Values](https://www.youtube.com/watch?v=fU2PuYKsr6M) `0:33:00`
- [ ] [Udacity: Algebra Review](https://www.udacity.com/course/intro-algebra-review--ma004)
- [ ] [Udacity: Differential Equations in Action](https://www.udacity.com/course/differential-equations-in-action--cs222)
- [ ] [Udacity: Eigenvectors and Eigenvalues](https://www.udacity.com/course/eigenvectors-and-eigenvalues--ud104)
- [ ] [Udacity: Linear Algebra Refresher](https://www.udacity.com/course/linear-algebra-refresher-course--ud953)
- [ ] [Udacity: Statistics](https://www.udacity.com/course/statistics--st095)
- [ ] [Udacity: Intro to Descriptive Statistics](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827)
- [ ] [Udacity: Intro to Inferential Statistics](https://www.udacity.com/course/intro-to-inferential-statistics--ud201)
- [ ] [Youtube: Principal Component Analysis (PCA) - THE MATH YOU SHOULD KNOW!](https://www.youtube.com/watch?v=9oSkUej63yk) `0:10:06`
- [ ] [Youtube: Support Vector Machines - THE MATH YOU  SHOULD KNOW](https://www.youtube.com/watch?v=05VABNfa1ds) `0:11:21`
- [ ] [Youtube: The Kernel Trick - THE MATH YOU SHOULD KNOW!](https://www.youtube.com/watch?v=wBVSbVktLIY) `0:07:29`
- [ ] [Youtube: Logistic Regression - THE MATH YOU SHOULD KNOW!](https://www.youtube.com/watch?v=YMJtsYIp4kg) `0:09:14`
- [ ] [Youtube: But what *is* a Neural Network? - THE MATH YOU SHOULD KNOW!](https://www.youtube.com/watch?v=oB3gmT8GAgI) `0:19:07`
- [ ] [Youtube: Visualizing Deep Learning](https://www.youtube.com/playlist?list=PLyPKqVSnetmEOp_g_hfabuRAs9ET-shl_)
- [ ] [The Neural Network, A Visual Introduction | Visualizing Deep Learning, Chapter 1](https://www.youtube.com/watch?v=UOvPeC8WOt8&feature=youtu.be)

### Be able to structure machine learning projects
- [ ] [Article: I trained a model. What is next?](https://ternaus.blog/tutorial/2020/08/28/Trained-model-what-is-next.html)
- [ ] [Article: pydantic](https://towardsdatascience.com/pydantic-688e897cfd3a)
- [ ] [Article: Always start with a stupid model, no exceptions.](https://mlpowered.com/posts/start-with-a-stupid-model/)
- [ ] [Article: Organizing machine learning projects: project management guidelines](https://www.jeremyjordan.me/ml-projects-guide/)
- [ ] [Article: Most impactful AI trends of 2018: the rise of ML Engineering](https://mlpowered.com/posts/2019-ml-trends/)
- [ ] [Article: Building machine learning products: a problem well-defined is a problem half-solved.](https://www.jeremyjordan.me/ml-requirements/)
- [ ] [Article: Simple considerations for simple people building fancy neural networks](https://medium.com/huggingface/simple-considerations-for-simple-people-building-fancy-neural-networks-7abc3c0f0bd7)
- [ ] [Article: Logging and Debugging in Machine Learning - How to use Python debugger and the logging module to find errors in your AI application](https://theaisummer.com/logging-debugging/)
- [ ] [Article: How to Unit Test Deep Learning: Tests in TensorFlow, mocking and test coverage](https://theaisummer.com/unit-test-deep-learning/)
- [ ] [Article: Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation](https://theaisummer.com/best-practices-deep-learning-code/)
- [ ] [Article: Deep Learning in Production: Laptop set up and system design](https://theaisummer.com/deep-learning-production/)
- [ ] [Article: Configuring Google Colab Like A Pro](https://medium.com/@robertbracco1/configuring-google-colab-like-a-pro-d61c253f7573)
- [ ] [Article: Stop using print, start using loguru in Python](https://handyman.dulare.com/stop-using-print-start-using-loguru-in-python/)
- [ ] [Coursera: Structuring Machine Learning Projects](https://www.coursera.org/learn/machine-learning-projects?specialization=deep-learning)
- [ ] [Datacamp: Conda Essentials](https://www.datacamp.com/courses/conda-essentials)
- [ ] [Datacamp: Conda for Building & Distributing Packages](https://www.datacamp.com/courses/conda-for-building-distributing-packages)
- [ ] [Datacamp: Creating Robust Python Workflows](https://www.datacamp.com/courses/creating-robust-python-workflows)
- [ ] [Datacamp: Software Engineering for Data Scientists in Python](https://www.datacamp.com/courses/software-engineering-for-data-scientists-in-python)
- [ ] [Datacamp: Designing Machine Learning Workflows in Python](https://www.datacamp.com/courses/designing-machine-learning-workflows-in-python)
- [ ] [Datacamp: Object-Oriented Programming in Python](https://www.datacamp.com/courses/object-oriented-programming-in-python)
- [ ] [Datacamp: Command Line Automation in Python](https://www.datacamp.com/courses/command-line-automation-in-python)
- [ ] [Datacamp: Introduction to Data Engineering](https://www.datacamp.com/courses/introduction-to-data-engineering)
- [ ] [Datacamp: Experimental Design in Python](https://www.datacamp.com/courses/experimental-design-in-python)
- [ ] [Full Stack Deep Learning Bootcamp: March 2019](https://fullstackdeeplearning.com/march2019)
  - [ ] [Lecture 1: Introduction to Deep Learning](https://youtu.be/5AjG5OPQuBM)
  - [ ] [Lecture 2: Setting Up Machine Learning Projects](https://youtu.be/tBUK1_cHu-8)
  - [ ] [Lecture 3: Introduction to the Text Recognizer Project](https://youtu.be/mmlvGLSXKLc)
  - [ ] [Lecture 4: Infrastructure and Tooling](https://youtu.be/f6jAz1zyrDI)
  - [ ] [Lecture 5: Tracking Experiments](https://youtu.be/Eiz1zcqrqw0)
  - [ ] [Lecture 6: Data Management](https://youtu.be/T5jv8-xZhZI)
  - [ ] [Lecture 7: Machine Learning Teams](https://youtu.be/Qb3RhwNb4EM)
  - [ ] [Lecture 9: Lukas Biewald](https://youtu.be/25_kBogrzrs)
  - [ ] [Lecture 10: Troubleshooting Deep Neural Networks](https://youtu.be/GwGTwPcG0YM)
  - [ ] [Lecture 11: Labs 6-9: Detection, Data Labeling, Testing and Deployment](https://youtu.be/JTSwQu0OyGs)
  - [ ] [Lecture 12: Testing and Deployment](https://youtu.be/nu7h1zdKPd0)
  - [ ] [Lecture 13: Research Directions](https://youtu.be/vF7UgqaegVI)
  - [ ] [Lecture 14: Jeremy Howard](https://youtu.be/hZd3X_nGdew)
  - [ ] [Lecture 15: Richard Socher](https://youtu.be/yvMgcLKuvVg)
  - [ ] [Guest Lecture - Chip Huyen - Machine Learning Interviews - Full Stack Deep Learning](https://youtu.be/pli1K75PSa8)
- [ ] [MIT: The Missing Semester of CS Education](https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J)
	- [ ] [Lecture 1: Course Overview + The Shell (2020)](https://www.youtube.com/watch?v=Z56Jmr9Z34Q) `0:48:16`
	- [ ] [Lecture 2: Shell Tools and Scripting (2020)](https://www.youtube.com/watch?v=kgII-YWo3Zw) `0:48:55`
	- [ ] [Lecture 3: Editors (vim) (2020)](https://www.youtube.com/watch?v=a6Q8Na575qc) `0:48:26`
	- [ ] [Lecture 4: Data Wrangling (2020)](https://www.youtube.com/watch?v=sz_dsktIjt4) `0:50:03`
	- [ ] [Lecture 5: Command-line Environment (2020)](https://www.youtube.com/watch?v=e8BO_dYxk5c) `0:56:06`
	- [ ] [Lecture 6: Version Control (git) (2020)](https://www.youtube.com/watch?v=2sjqTHE0zok) `1:24:59`
	- [ ] [Lecture 7: Debugging and Profiling (2020)](https://www.youtube.com/watch?v=l812pUnKxME) `0:54:13`
	- [ ] [Lecture 8: Metaprogramming (2020)](https://www.youtube.com/watch?v=_Ms1Z4xfqv4) `0:49:52`
	- [ ] [Lecture 9: Security and Cryptography (2020)](https://www.youtube.com/watch?v=tjwobAmnKTo) `1:00:59`
	- [ ] [Lecture 10: Potpourri (2020)](https://www.youtube.com/watch?v=JZDt-PRq0uo) `0:57:54`
	- [ ] [Lecture 11: Q&A (2020)](https://www.youtube.com/watch?v=Wz50FvGG6xU) `0:53:52`
- [ ] [Treehouse: Object Oriented Python](https://teamtreehouse.com/library/objectoriented-python-2)
- [ ] [Treehouse: Setup Local Python Environment](https://teamtreehouse.com/library/setting-utorialtorialp-a-local-python-environment-windows)
- [ ] [Udacity: Writing READMEs](https://www.udacity.com/course/writing-readmes--ud777)
- [ ] [Youtube: Weights and Biases Tutorial](https://www.youtube.com/playlist?list=PLD80i8An1OEE0xs5BjKpCBdm0aaDI00U9)
- [ ] [Youtube: Integrate Weights & Biases with PyTorch](https://www.youtube.com/watch?v=G7GH0SeNBMA&list=PLD80i8An1OEGajeVo15ohAQYF1Ttle0lk&index=2)
- [ ] [Youtube: Log (Almost) Anything with Weights & Biases](https://www.youtube.com/watch?v=96MxRvx15Ts&list=PLD80i8An1OEGajeVo15ohAQYF1Ttle0lk&index=3)
- [ ] [Youtube: MLOps Tutorials](https://www.youtube.com/playlist?list=PL7WG7YrwYcnDBDuCkFbcyjnZQrdskFsBz)
    - [ ] [MLOps Tutorial #1: Intro to Continuous Integration for ML](https://youtu.be/9BgIDqAzfuA?list=PL7WG7YrwYcnDBDuCkFbcyjnZQrdskFsBz) `17:44:00`
    - [ ] [MLOps Tutorial #2: When data is too big for Git](https://youtu.be/kZKAuShWF0s?list=PL7WG7YrwYcnDBDuCkFbcyjnZQrdskFsBz) `10:51:00`
    - [ ] [MLOps Tutorial #3: Track ML models with Git & GitHub Actions](https://youtu.be/xPncjKH6SPk?list=PL7WG7YrwYcnDBDuCkFbcyjnZQrdskFsBz) `14:12:00`
- [ ] [Youtube: Data Engineering + ML + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16](https://www.youtube.com/watch?v=IUhvO8QjWd8&list=LL&index=2)
- [ ] [Youtube: OO Design and Testing Patterns for Machine Learning with Chris Gerpheide](https://www.youtube.com/watch?v=VuQaS0FFUFY)
- [ ] [Youtube: MLSys Seminars Fall 2020](https://www.youtube.com/playlist?list=PLSrTvUm384I9PV10koj_cqit9OfbJXEkq)
    - [ ] [Stanford MLSys Seminar Episode 0: ML + Systems](https://www.youtube.com/watch?v=OEiNnfdxBRE) `0:11:49`
    - [ ] [Stanford MLSys Seminar Episode 1: Marco Tulio Ribeiro](https://www.youtube.com/watch?v=VqiTtdY58Ts) `1:00:38`
    - [ ] [Stanford MLSys Seminar Episode 2: Matei Zaharia](https://www.youtube.com/watch?v=nCQ9WqXPIS4) `0:59:44`
    - [ ] [Stanford MLSys Seminar Episode 3: Virginia Smith](https://www.youtube.com/watch?v=laCyJICLyWg) `1:00:55`
    - [ ] [Stanford MLSys Seminar Episode 4: Alex Ratner](https://www.youtube.com/watch?v=pDVV4zKNqIE) `1:13:34`
    - [ ] [Stanford MLSys Seminar Episode 5: Chip Huyen](https://www.youtube.com/watch?v=c_AUuTuPA5k) `1:06:44`

### Be able to version control code
- [ ] [Article: Mastering Git Stash Workflow](https://dev.to/yankee/mastering-git-stash-workflow-223)
- [ ] [Article: How to Become a Master of Git Tags](https://blog.daftcode.pl/how-to-become-a-master-of-git-tags-b70fbd9609d9)
- [ ] [Article: How to track large files in Github / Bitbucket? Git LFS to the rescue](https://pgaijin66.medium.com/how-to-track-large-files-in-github-bitbucket-git-lfs-to-the-rescue-2f298e07b3d2)
- [ ] [Article: Keep your git directory clean with `git clean` and `git trash`](https://coderwall.com/p/g16jpq/keep-your-git-directory-clean-with-git-clean-and-git-trash)
- [ ] [Codecademy: Learn Git](https://www.codecademy.com/learn/learn-git)
- [ ] [Code School: Git Real](https://www.pluralsight.com/courses/code-school-git-real)
- [ ] [Datacamp: Introduction to Git for Data Science](https://www.datacamp.com/courses/introduction-to-git-for-data-science)
- [ ] [Learn enough git to be dangerous](http://learnenough.com/git-tutorial)
- [ ] [Learn Git Branching](https://learngitbranching.js.org)
- [ ] [Thoughtbot: Mastering Git](https://thoughtbot.com/upcase/mastering-git)
- [ ] [Udacity: GitHub & Collaboration](https://www.udacity.com/course/github-collaboration--ud456)
- [ ] [Udacity: How to Use Git and GitHub](https://www.udacity.com/course/how-to-use-git-and-github--ud775)
- [ ] [Udacity: Version Control with Git](https://www.udacity.com/course/version-control-with-git--ud123)
- [ ] [Youtube: 045 Introduction to Git LFS](https://youtu.be/xPFLAAhuGy0)
 
### Be able to use version control data
- [ ] [Youtube: DVC Basics](https://www.youtube.com/playlist?list=PL7WG7YrwYcnDb0qdPl9-KEStsL-3oaEjg)
	- [ ] [Versioning Data with DVC (Hands-On Tutorial!)](https://www.youtube.com/watch?v=kLKBcPonMYw) `0:13:04`
	- [ ] [Sharing Data and Models with DVC (Hands-On Data Science Tutorial!)](https://www.youtube.com/watch?v=EE7Gk84OZY8) `0:08:53`
	- [ ] [Machine Learning Pipelines with DVC (Hands-On Tutorial!)](https://www.youtube.com/watch?v=71IGzyH95UY) `0:10:04`
	- [ ] [Machine Learning Experiments with DVC (Hands-On Tutorial!)](https://www.youtube.com/watch?v=iduHPtBncBk) `0:07:29`

### Be familiar with fundamental of ML and DL
- [ ] [Article: Validating your Machine Learning Model](https://www.maartengrootendorst.com/blog/validate/)
- [ ] [Article: One-vs-Rest strategy for Multi-Class Classification](https://www.geeksforgeeks.org/one-vs-rest-strategy-for-multi-class-classification/)
- [ ] [Article: Multi-class Classification — One-vs-All & One-vs-One](https://radiant-brushlands-42789.herokuapp.com/towardsdatascience.com/multi-class-classification-one-vs-all-one-vs-one-94daed32a87b)
- [ ] [Article: One-vs-Rest and One-vs-One for Multi-Class Classification](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
- [ ] [Article: The Complete Guide to AUC and Average Precision: Simulations and Visualizations](https://glassboxmedicine.com/2020/07/14/the-complete-guide-to-auc-and-average-precision-simulations-and-visualizations/)
- [ ] [Article: Connections: Log Likelihood, Cross Entropy, KL Divergence, Logistic Regression, and Neural Networks](https://glassboxmedicine.com/2019/12/07/connections-log-likelihood-cross-entropy-kl-divergence-logistic-regression-and-neural-networks/)
- [ ] [Article: Best Use of Train/Val/Test Splits, with Tips for Medical Data](https://glassboxmedicine.com/2019/09/15/best-use-of-train-val-test-splits-with-tips-for-medical-data/)
- [ ] [Article: Measuring Performance: AUPRC and Average Precision](https://glassboxmedicine.com/2019/03/02/measuring-performance-auprc/)
- [ ] [Article: Measuring Performance: AUC (AUROC)](https://glassboxmedicine.com/2019/02/23/measuring-performance-auc-auroc/)
- [ ] [Article: Measuring Performance: The Confusion Matrix](https://glassboxmedicine.com/2019/02/17/measuring-performance-the-confusion-matrix/)
- [ ] [Article: Measuring Performance: Accuracy](https://glassboxmedicine.com/2019/02/16/measuring-performance-accuracy/)
- [ ] [Article: Naive Bayes classification](https://www.jeremyjordan.me/naive-bayes-classification/)
- [ ] [Article: Linear regression](https://www.jeremyjordan.me/linear-regression/)
- [ ] [Article: Polynomial regression](https://www.jeremyjordan.me/polynomial-regression/)
- [ ] [Article: Logistic regression](https://www.jeremyjordan.me/logistic-regression/)
- [ ] [Article: Decision trees](https://www.jeremyjordan.me/decision-trees/)
- [ ] [Article: K-nearest neighbors](https://www.jeremyjordan.me/k-nearest-neighbors/)
- [ ] [Article: Support Vector Machines](https://www.jeremyjordan.me/support-vector-machines/)
- [ ] [Article: Random forests](https://www.jeremyjordan.me/random-forests/)
- [ ] [Article: Boosted trees](https://www.jeremyjordan.me/boosted-trees/)
- [ ] [Article: ROC Curves: Intuition Through Visualization](https://www.pragmatic.ml/intuition-through-visualization-roc-auc/)
- [ ] [Article: Hacker's Guide to Fundamental Machine Learning Algorithms with Python](https://www.curiousily.com/posts/hackers-guide-to-fundamental-machine-learning-algorithms/)
- [ ] [Article: Simple Ways to Tackle Class Imbalance](https://wandb.ai/authors/class-imbalance/reports/Simple-Ways-to-Tackle-Class-Imbalance--VmlldzoxODA3NTk)
- [ ] [Article: MLE and MAP — in layman’s terms](https://medium.com/@ajitrajasekharan/mle-and-map-in-laymans-terms-8527061b15a6)
- [ ] [Article: The Last 5 Years In Deep Learning](https://adeshpande3.github.io/adeshpande3.github.io/The-Last-5-Years-in-Deep-Learning)
- [ ] [Article: Visualizing Optimization Trajectory of Neural Nets](https://towardsdatascience.com/from-animation-to-intuition-visualizing-optimization-trajectory-in-neural-nets-726e43a08d85)
- [ ] [An overview of gradient descent optimization algorithms](https://ruder.io/optimizing-gradient-descent/)
- [ ] [Article: Optimization for Deep Learning Highlights in 2017](https://ruder.io/deep-learning-optimization-2017/)
- [ ] [Article: Neural networks: activation functions](https://www.jeremyjordan.me/neural-networks-activation-functions/)
- [ ] [Article: Neural networks: training with backpropagation](https://www.jeremyjordan.me/neural-networks-training/)
- [ ] [Article: Gradient descent](https://www.jeremyjordan.me/gradient-descent/)
- [ ] [Article: Setting the learning rate of your neural network](https://www.jeremyjordan.me/nn-learning-rate/)
- [ ] [Article: Deep neural networks: preventing overfitting](https://www.jeremyjordan.me/deep-neural-networks-preventing-overfitting/)
- [ ] [Article: Normalizing your data (specifically, input and batch normalization)](https://www.jeremyjordan.me/batch-normalization/)
- [ ] [Article: Batch Normalization](https://e2eml.school/batch_normalization.html)
- [ ] [Article: Are Deep Neural Networks Dramatically Overfitted?](https://lilianweng.github.io/lil-log/2019/03/14/are-deep-neural-networks-dramatically-overfitted.html)
- [ ] [Article: Attention? Attention!](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)
- [ ] [Article: How to Explain the Prediction of a Machine Learning Model?](https://lilianweng.github.io/lil-log/2017/08/01/how-to-explain-the-prediction-of-a-machine-learning-model.html)
- [ ] [Article: Neural Network from scratch-part 1](https://theaisummer.com/Neural_Network_from_scratch/)
- [ ] [Article: Neural Network from scratch-part 2](https://theaisummer.com/Neural_Network_from_scratch_part2/)
- [ ] [Article: Deep Learning Algorithms - The Complete Guide](https://theaisummer.com/Deep-Learning-Algorithms/)
- [ ] [Article: In-layer normalization techniques for training very deep neural networks](https://theaisummer.com/normalization/)
- [ ] [Article: Cross-entropy for classification](https://towardsdatascience.com/cross-entropy-for-classification-d98e7f974451)
- [ ] [Article: Perceptron to Deep-Neural-Network](https://tsumansapkota.github.io/algorithm/2020/06/06/Perceptron-to-DeepNeuralNets/)
- [ ] [Article: Dismantling Neural Networks to Understand the Inner Workings with Math and Pytorch](https://towardsdatascience.com/dismantling-neural-networks-to-understand-the-inner-workings-with-math-and-pytorch-beac8760b595)
- [ ] [Article: Precision, Recall, Accuracy, and F1 Score for Multi-Label Classification](https://medium.com/synthesio-engineering/precision-accuracy-and-f1-score-for-multi-label-classification-34ac6bdfb404)
- [ ] [AWS: The Elements of Data Science](https://www.aws.training/learningobject/wbc?id=26598)
- [ ] [AWS: Understanding Neural Networks](https://www.aws.training/learningobject/video?id=27233)
- [ ] [Book: Approaching (Almost) Any Machine Learning Problem](https://github.com/abhishekkrthakur/approachingalmost)
- [ ] [Book: Pattern Recognition and Machine Learning](https://www.goodreads.com/book/show/55881.Pattern_Recognition_and_Machine_Learning)
- [ ] [Book: Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning)
- [ ] [Book: Make Your Own Neural Network](https://www.amazon.com/Make-Your-Own-Neural-Network-ebook/dp/B01EER4Z4G)
- [ ] [Coursera: Neural Networks and Deep Learning](https://www.coursera.org/learn/neural-networks-deep-learning)
- [ ] [Datacamp: AI Fundamentals](https://www.datacamp.com/courses/fundamentals-of-ai)
- [ ] [Datacamp: Extreme Gradient Boosting with XGBoost](https://www.datacamp.com/courses/extreme-gradient-boosting-with-xgboost)
- [ ] [Datacamp: Ensemble Methods in Python](https://www.datacamp.com/courses/ensemble-methods-in-python)
- [ ] [Datacamp: Foundations of Predictive Analytics in Python (Part 1)](https://www.datacamp.com/courses/foundations-of-predictive-analytics-in-python-part-1)
- [ ] [Datacamp: Foundations of Predictive Analytics in Python (Part 2)](https://www.datacamp.com/courses/foundations-of-predictive-analytics-in-python-part-2)
- [ ] [DeepMind: DeepMind x UCL | Deep Learning Lecture Series 2020](https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF)
	- [ ] [DeepMind x UCL | Deep Learning Lectures | 1/12 | Intro to Machine Learning & AI](https://www.youtube.com/watch?v=7R52wiUgxZI) `1:25:17`
	- [ ] [DeepMind x UCL | Deep Learning Lectures | 2/12 |  Neural Networks Foundations](https://www.youtube.com/watch?v=FBggC-XVF4M) `1:24:12`
	- [ ] [DeepMind x UCL | Deep Learning Lectures | 3/12 | Convolutional Neural Networks for Image Recognition](https://www.youtube.com/watch?v=shVKhOmT0HE) `1:20:19`
	- [ ] [DeepMind x UCL | Deep Learning Lectures | 4/12 |  Advanced Models for Computer Vision](https://www.youtube.com/watch?v=_aUq7lmMfxo) `1:33:37`
	- [ ] [DeepMind x UCL | Deep Learning Lectures | 5/12 |  Optimization for Machine Learning](https://www.youtube.com/watch?v=kVU8zTI-Od0) `1:30:21`
	- [ ] [DeepMind x UCL | Deep Learning Lectures | 6/12 | Sequences and Recurrent Networks](https://www.youtube.com/watch?v=87kLfzmYBy8) `1:20:27`
	- [ ] [DeepMind x UCL | Deep Learning Lectures | 7/12 |  Deep Learning for Natural Language Processing](https://www.youtube.com/watch?v=8zAP2qWAsKg) `1:32:29`
	- [ ] [DeepMind x UCL | Deep Learning Lectures | 8/12 |  Attention and Memory in Deep Learning](https://www.youtube.com/watch?v=AIiwuClvH6k) `1:36:04`
	- [ ] [DeepMind x UCL | Deep Learning Lectures | 9/12 |  Generative Adversarial Networks](https://www.youtube.com/watch?v=wFsI2WqUfdA) `1:42:26`
	- [ ] [DeepMind x UCL | Deep Learning Lectures | 10/12 |  Unsupervised Representation Learning](https://www.youtube.com/watch?v=f0s-uvvXvWg) `1:44:40`
	- [ ] [DeepMind x UCL | Deep Learning Lectures | 11/12 | Modern Latent Variable Models](https://www.youtube.com/watch?v=7Pcvdo4EJeo) `1:28:26`
	- [ ] [DeepMind x UCL | Deep Learning Lectures | 12/12 |  Responsible Innovation](https://www.youtube.com/watch?v=MhNcWxUs-PQ) `1:02:28`
- [ ] [Elements of AI](https://www.elementsofai.com)
- [ ] [edX: Principles of Machine Learning](https://www.edx.org/course/principles-machine-learning-microsoft-dat203-2x-5)
- [ ] [edX: Data Science Essentials](https://www.edx.org/course/data-science-essentials-microsoft-dat203-1x-5)
- [ ] [Fast.ai: Deep Learning for Coder (2020)](https://course.fast.ai/)
    - [ ] [Lesson 1](https://course.fast.ai/videos/?lesson=1)
    - [ ] [Lesson 2](https://course.fast.ai/videos/?lesson=2)
    - [ ] [Lesson 3](https://course.fast.ai/videos/?lesson=3)
    - [ ] [Lesson 4](https://course.fast.ai/videos/?lesson=4)
    - [ ] [Lesson 5](https://course.fast.ai/videos/?lesson=5)
    - [ ] [Lesson 6](https://course.fast.ai/videos/?lesson=6)
    - [ ] [Lesson 7](https://course.fast.ai/videos/?lesson=7)
    - [ ] [Lesson 8](https://course.fast.ai/videos/?lesson=8)
- [ ] [MIT: 6.S191: Introduction to Deep Learning](http://introtodeeplearning.com/#schedule)
	- [ ] [MIT Introduction to Deep Learning | 6.S191](https://www.youtube.com/watch?v=njKP3FqW3Sk) `0:52:51`
	- [ ] [Recurrent Neural Networks | MIT 6.S191](https://www.youtube.com/watch?v=SEnXr6v2ifU) `0:45:28`
	- [ ] [Convolutional Neural Networks | MIT 6.S191](https://www.youtube.com/watch?v=iaSUYvmCekI) `0:37:20`
	- [ ] [Deep Generative Modeling | MIT 6.S191](https://www.youtube.com/watch?v=rZufA635dq4) `0:40:39`
	- [ ] [Reinforcement Learning | MIT 6.S191](https://www.youtube.com/watch?v=nZfaHIxDD5w) `0:44:11`
	- [ ] [Deep Learning New Frontiers | MIT 6.S191](https://www.youtube.com/watch?v=tfM_DdbGTLs) `0:38:10`
	- [ ] [Neurosymbolic AI | MIT 6.S191](https://www.youtube.com/watch?v=4PuuziOgSU4) `0:41:10`
	- [ ] [Generalizable Autonomy for Robot Manipulation | MIT 6.S191](https://www.youtube.com/watch?v=8Kn4Gi8iSYQ) `0:47:00`
	- [ ] [Neural Rendering | MIT 6.S191](https://www.youtube.com/watch?v=BCZ56MU-KhQ) `0:36:44`
	- [ ] [Machine Learning for Scent | MIT 6.S191](https://www.youtube.com/watch?v=Z5Pw5eWItiw) `0:38:51`
- [ ] [Pluralsight: Deep Learning: The Big Picture](https://www.pluralsight.com/courses/deep-learning-big-picture)
- [ ] [StatQuest: Machine Learning](https://www.youtube.com/playlist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF)
	- [ ] [A Gentle Introduction to Machine Learning](https://www.youtube.com/watch?v=Gv9_4yMHFhI) `0:12:45`
	- [ ] [Machine Learning Fundamentals: Cross Validation](https://www.youtube.com/watch?v=fSytzGwwBVw) `0:06:04`
	- [ ] [Machine Learning Fundamentals: The Confusion Matrix](https://www.youtube.com/watch?v=Kdsp6soqA7o) `0:07:12`
	- [ ] [Machine Learning Fundamentals: Sensitivity and Specificity](https://www.youtube.com/watch?v=vP06aMoz4v8) `0:11:46`
	- [ ] [Machine Learning Fundamentals: Bias and Variance](https://www.youtube.com/watch?v=EuBBz3bI-aA) `0:06:36`
	- [ ] [ROC and AUC, Clearly Explained!](https://www.youtube.com/watch?v=4jRBRDbJemM) `0:16:26`
	- [ ] [StatQuest: Fitting a line to data, aka least squares, aka linear regression.](https://www.youtube.com/watch?v=PaFPbb66DxQ) `0:09:21`
	- [ ] [StatQuest: Linear Models Pt.1 - Linear Regression](https://www.youtube.com/watch?v=nk2CQITm_eo) `0:27:26`
	- [ ] [StatQuest: Odds and Log(Odds), Clearly Explained!!!](https://www.youtube.com/watch?v=ARfXDSkQf1Y) `0:11:30`
	- [ ] [StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!!](https://www.youtube.com/watch?v=8nm0G-1uJzA) `0:16:20`
	- [ ] [StatQuest: Logistic Regression](https://www.youtube.com/watch?v=yIYKR4sgzI8) `0:08:47`
	- [ ] [Logistic Regression Details Pt1: Coefficients](https://www.youtube.com/watch?v=vN5cNN2-HWE) `0:19:02`
	- [ ] [Logistic Regression Details Pt 2: Maximum Likelihood](https://www.youtube.com/watch?v=BfKanl1aSG0) `0:10:23`
	- [ ] [Logistic Regression Details Pt 3: R-squared and p-value](https://www.youtube.com/watch?v=xxFYro8QuXA) `0:15:25`
	- [ ] [Saturated Models and Deviance](https://www.youtube.com/watch?v=9T0wlKdew6I) `0:18:39`
	- [ ] [Deviance Residuals](https://www.youtube.com/watch?v=JC56jS2gVUE) `0:06:18`
	- [ ] [Regularization Part 1: Ridge (L2) Regression](https://www.youtube.com/watch?v=Q81RR3yKn30) `0:20:26`
	- [ ] [Regularization Part 2: Lasso (L1) Regression](https://www.youtube.com/watch?v=NGf0voTMlcs) `0:08:19`
	- [ ] [Ridge vs Lasso Regression, Visualized!!!](https://www.youtube.com/watch?v=Xm2C_gTAl8c) `0:09:05`
	- [ ] [Regularization Part 3: Elastic Net Regression](https://www.youtube.com/watch?v=1dKRdX9bfIo) `0:05:19`
	- [ ] [StatQuest: Principal Component Analysis (PCA), Step-by-Step](https://www.youtube.com/watch?v=FgakZw6K1QQ) `0:21:57`
	- [ ] [StatQuest: PCA main ideas in only 5 minutes!!!](https://www.youtube.com/watch?v=HMOI_lkzW08) `0:06:04`
	- [ ] [StatQuest: PCA - Practical Tips](https://www.youtube.com/watch?v=oRvgq966yZg) `0:08:19`
	- [ ] [StatQuest: PCA in Python](https://www.youtube.com/watch?v=Lsue2gEM9D0) `0:11:37`
	- [ ] [StatQuest: Linear Discriminant Analysis (LDA) clearly explained.](https://www.youtube.com/watch?v=azXCzI57Yfc) `0:15:12`
	- [ ] [StatQuest: MDS and PCoA](https://www.youtube.com/watch?v=GEn-_dAyYME) `0:08:18`
	- [ ] [StatQuest: t-SNE, Clearly Explained](https://www.youtube.com/watch?v=NEaUSP4YerM) `0:11:47`
	- [ ] [StatQuest: Hierarchical Clustering](https://www.youtube.com/watch?v=7xHsRkOdVwo) `0:11:19`
	- [ ] [StatQuest: K-means clustering](https://www.youtube.com/watch?v=4b5d3muPQmA) `0:08:57`
	- [ ] [StatQuest: K-nearest neighbors, Clearly Explained](https://www.youtube.com/watch?v=HVXime0nQeI) `0:05:30`
	- [ ] [Naive Bayes, Clearly Explained!!!](https://www.youtube.com/watch?v=O2L2Uv9pdDA) `0:15:12`
	- [ ] [Gaussian Naive Bayes, Clearly Explained!!!](https://www.youtube.com/watch?v=H3EjCKtlVog) `0:09:41`
	- [ ] [StatQuest: Decision Trees](https://www.youtube.com/watch?v=7VeUPuFGJHk) `0:17:22`
	- [ ] [StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data](https://www.youtube.com/watch?v=wpNl-JwwplA) `0:05:16`
	- [ ] [Regression Trees, Clearly Explained!!!](https://www.youtube.com/watch?v=g9c66TUylZ4) `0:22:33`
	- [ ] [How to Prune Regression Trees, Clearly Explained!!!](https://www.youtube.com/watch?v=D0efHEJsfHo) `0:16:15`
	- [ ] [StatQuest: Random Forests Part 1 - Building, Using and Evaluating](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ) `0:09:54`
	- [ ] [StatQuest: Random Forests Part 2: Missing data and clustering](https://www.youtube.com/watch?v=sQ870aTKqiM) `0:11:53`
	- [ ] [The Chain Rule](https://www.youtube.com/watch?v=wl1myxrtQHQ) `0:18:23`
	- [ ] [Gradient Descent, Step-by-Step](https://www.youtube.com/watch?v=sDv4f4s2SB8) `0:23:54`
	- [ ] [Stochastic Gradient Descent, Clearly Explained!!!](https://www.youtube.com/watch?v=vMh0zPT0tLI) `0:10:53`
	- [ ] [AdaBoost, Clearly Explained](https://www.youtube.com/watch?v=LsK-xG1cLYA) `0:20:54`
	- [ ] [Gradient Boost Part 1: Regression Main Ideas](https://www.youtube.com/watch?v=3CC4N4z3GJc) `0:15:52`
	- [ ] [Gradient Boost Part 2: Regression Details](https://www.youtube.com/watch?v=2xudPOBz-vs) `0:26:45`
	- [ ] [Gradient Boost Part 3: Classification](https://www.youtube.com/watch?v=jxuNLH5dXCs) `0:17:02`
	- [ ] [Gradient Boost Part 4: Classification Details](https://www.youtube.com/watch?v=StWY5QWMXCw) `0:36:59`
	- [ ] [Bam!!! Clearly Explained!!!](https://www.youtube.com/watch?v=i4iUvjsGCMc) `0:02:49`
	- [ ] [Support Vector Machines, Clearly Explained!!!](https://www.youtube.com/watch?v=efR1C6CvhmE) `0:20:32`
	- [ ] [Support Vector Machines Part 2: The Polynomial Kernel](https://www.youtube.com/watch?v=Toet3EiSFcM) `0:07:15`
	- [ ] [Support Vector Machines Part 3: The Radial (RBF) Kernel](https://www.youtube.com/watch?v=Qc5IyLW_hns) `0:15:52`
	- [ ] [XGBoost Part 1: Regression](https://www.youtube.com/watch?v=OtD8wVaFm6E) `0:25:46`
	- [ ] [XGBoost Part 2: Classification](https://www.youtube.com/watch?v=8b1JEDvenQU) `0:25:17`
	- [ ] [XGBoost Part 3: Mathematical Details](https://www.youtube.com/watch?v=ZVFeW798-2I) `0:27:24`
	- [ ] [XGBoost Part 4: Crazy Cool Optimizations](https://www.youtube.com/watch?v=oRrKeUCEbq8) `0:24:27`
	- [ ] [StatQuest: Fiitting a curve to data, aka lowess, aka loess](https://www.youtube.com/watch?v=Vf7oJ6z2LCc) `0:10:10`
	- [ ] [Statistics Fundamentals: Population Parameters](https://www.youtube.com/watch?v=vikkiwjQqfU) `0:14:31`
	- [ ] [Principal Component Analysis (PCA) clearly explained (2015)](https://www.youtube.com/watch?v=_UVHneBUBW0) `0:20:16`
	- [ ] [Decision Trees in Python from Start to Finish](https://www.youtube.com/watch?v=q90UDEgYqeI) `1:06:23`
- [ ] [Udacity: Deep Learning](https://www.udacity.com/course/deep-learning--ud730)
- [ ] [Udacity: A Friendly Introduction to Machine Learning](https://www.youtube.com/playlist?list=PLAwxTw4SYaPknYBrOQx6UCyq67kprqXe3)
- [ ] [Udacity: Intro to Data Analysis](https://www.udacity.com/course/intro-to-data-analysis--ud170)
- [ ] [Udacity: Intro to Data Science](https://www.udacity.com/course/intro-to-data-science--ud359)
- [ ] [Udacity: Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120)
- [ ] [Udacity: Classification Models](https://www.udacity.com/course/classification-models--ud978)
- [ ] [Youtube: Neural Networks from Scratch in Python](https://www.youtube.com/playlist?list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3)
	- [ ] [Neural Networks from Scratch - P.1 Intro and Neuron Code](https://www.youtube.com/watch?v=Wo5dMEP_BbI) `0:16:59`
	- [ ] [Neural Networks from Scratch - P.2 Coding a Layer](https://www.youtube.com/watch?v=lGLto9Xd7bU) `0:15:06`
	- [ ] [Neural Networks from Scratch - P.3 The Dot Product](https://www.youtube.com/watch?v=tMrbN67U9d4) `0:25:17`
	- [ ] [Neural Networks from Scratch - P.4 Batches, Layers, and Objects](https://www.youtube.com/watch?v=TEWy9vZcxW4) `0:33:46`
	- [ ] [Neural Networks from Scratch - P.5 Hidden Layer Activation Functions](https://www.youtube.com/watch?v=gmjzbpSVY1A) `0:40:05`
- [ ] [Youtube: Visualizing Deep Learning](https://www.youtube.com/playlist?list=PLyPKqVSnetmEOp_g_hfabuRAs9ET-shl_)
- [ ] [The Neural Network, A Visual Introduction | Visualizing Deep Learning, Chapter 1](https://youtu.be/UOvPeC8WOt8?list=PLyPKqVSnetmEOp_g_hfabuRAs9ET-shl_)
- [ ] [Youtube: Deep Double Descent](https://youtu.be/R29awq6jvUw)
- [ ] [Youtube: How do we check if a neural network has learned a specific phenomenon?](https://www.youtube.com/watch?v=fL22NAtMNYo)
- [ ] [Article: Label Smoothing Explained using Microsoft Excel](https://amaarora.github.io/2020/07/18/label-smoothing.html)
- [ ] [Youtube: What is Adversarial Machine Learning and what to do about it? – Adversarial example compilation](https://youtu.be/YyTyWGUUhmo)

### Be able to implement models in scikit-learn
- [ ] [Article: Stacking made easy with Sklearn](https://www.maartengrootendorst.com/blog/stacking/)
- [ ] [Calmcode: human-learn](https://calmcode.io/human-learn/introduction.html)
- [ ] [Datacamp: Supervised Learning with scikit-learn](https://www.datacamp.com/courses/supervised-learning-with-scikit-learn)
- [ ] [Datacamp: Machine Learning with Tree-Based Models in Python](https://www.datacamp.com/courses/machine-learning-with-tree-based-models-in-python)
- [ ] [Datacamp: Introduction to Linear Modeling in Python](https://www.datacamp.com/courses/introduction-to-linear-modeling-in-python)
- [ ] [Datacamp: Linear Classifiers in Python](https://www.datacamp.com/courses/linear-classifiers-in-python)
- [ ] [Datacamp: Generalized Linear Models in Python](https://www.datacamp.com/courses/generalized-linear-models-in-python)
- [ ] [Notebook: scikit-learn tips](https://github.com/justmarkham/scikit-learn-tips)
- [ ] [Pluralsight: Building Machine Learning Models in Python with scikit-learn](https://www.pluralsight.com/courses/python-scikit-learn-building-machine-learning-models)
- [ ] [Video: human learn](https://calmcode.io/human-learn/introduction.html)
- [ ] [Youtube: Applied Machine Learning 2020](https://www.youtube.com/playlist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM)
	- [ ] [Channel Intro - Applied Machine Learning](https://www.youtube.com/watch?v=d79mzijMAw0) `0:01:28`
	- [ ] [Applied ML 2020 - 01 Introduction](https://www.youtube.com/watch?v=rbvpiPJuK64) `1:16:01`
	- [ ] [Applied ML 2020 - 02 Visualization and matplotlib](https://www.youtube.com/watch?v=OW3oco7nlV4) `1:07:30`
	- [ ] [Applied ML 2020 - 03 Supervised learning and model validation](https://www.youtube.com/watch?v=7_YzyMYC2zM) `1:12:00`
	- [ ] [Applied ML 2020 - 04 - Preprocessing](https://www.youtube.com/watch?v=XpOBSaktb6s) `1:07:40`
	- [ ] [Applied ML 2020 - 05 - Linear Models for Regression](https://www.youtube.com/watch?v=-OOsfj5Revo) `1:06:54`
	- [ ] [Applied ML 2020 - 06 - Linear Models for Classification](https://www.youtube.com/watch?v=_dqBhUrq09U) `1:07:50`
	- [ ] [Applied ML 2020 - 07 - Decision Trees and Random Forests](https://www.youtube.com/watch?v=nomd5ylZ2dw) `1:07:58`
	- [ ] [Applied ML 2020 - 08 - Gradient Boosting](https://www.youtube.com/watch?v=yrTW5YTmFjw) `1:02:12`
	- [ ] [Applied ML 2020 - 09 - Model Evaluation and Metrics](https://www.youtube.com/watch?v=trg3YkCsjqE) `1:18:23`
	- [ ] [Applied ML 2020 - 10 - Calibration, Imbalanced data](https://www.youtube.com/watch?v=w3OPq0V8fr8) `1:16:14`
	- [ ] [Applied ML 2020 - 11 - Model Inspection and Feature Selection](https://www.youtube.com/watch?v=FDhyS6Xjxa8) `1:15:15`
	- [ ] [Applied ML 2020 - 12 - AutoML (plus some feature selection)](https://www.youtube.com/watch?v=bmBezdqHTAg) `1:25:38`
	- [ ] [Applied ML 2020 - 13 - Dimensionality reduction](https://www.youtube.com/watch?v=CrFOGyU32PM) `1:30:34`
	- [ ] [Applied ML 2020 - 14 - Clustering and Mixture Models](https://www.youtube.com/watch?v=HFioJ62H7dM) `1:26:33`
	- [ ] [Applied ML 2020 - 15 - Working with Text Data](https://www.youtube.com/watch?v=A8yDjNsUQJA) `1:27:08`
	- [ ] [Applied ML 2020 - 16 - Topic models for text data](https://www.youtube.com/watch?v=xdmFx4-3Ukw) `1:18:34`
	- [ ] [Applied ML 2020 - 17 - Word vectors and document embeddings](https://www.youtube.com/watch?v=xgjnlGBpLUs) `1:03:04`
	- [ ] [Applied ML 2020 - 18 - Neural Networks](https://www.youtube.com/watch?v=CRRPLlgYWZw) `1:19:36`
	- [ ] [Applied ML 2020 - 19 - Keras and Convolutional neural nets](https://www.youtube.com/watch?v=PP7Hr3tGbIo) `1:16:01`
	- [ ] [Applied ML 2020 - 20 - Advanced neural networks](https://www.youtube.com/watch?v=2FNmbX901r0) `1:36:28`
	- [ ] [Applied ML 2020 - 21 - Time Series and Forecasting](https://www.youtube.com/watch?v=GVGEnaJsuu8) `1:12:36`
- [ ] [Youtube: dabl: Automatic Machine Learning with a Human in the Loop](https://www.youtube.com/watch?v=TQnxH90PqFc&list=PLYx7XA2nY5GejOB1lsvriFeMytD1-VS1B&index=2&t=0s) `00:25:43`

### Be able to implement models in Tensorflow and Keras
- [ ] [Coursera: Introduction to Tensorflow](https://www.coursera.org/learn/introduction-tensorflow)
- [ ] [Coursera: Convolutional Neural Networks in TensorFlow](https://www.coursera.org/learn/convolutional-neural-networks-tensorflow)
- [ ] [Coursera: Getting Started With Tensorflow 2](https://www.coursera.org/learn/getting-started-with-tensor-flow2)
- [ ] [Coursera: Customising your models with TensorFlow 2](https://www.coursera.org/learn/customising-models-tensorflow2)
- [ ] [Deeplizard: Keras - Python Deep Learning Neural Network API](https://www.youtube.com/playlist?list=PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL)
- [ ] [Book: Deep Learning with Python (Page: 276)](https://www.manning.com/books/deep-learning-with-python)
- [ ] [Datacamp: Deep Learning in Python](https://www.datacamp.com/courses/deep-learning-in-python)
- [ ] [Datacamp: Convolutional Neural Networks for Image Processing](https://www.datacamp.com/courses/convolutional-neural-networks-for-image-processing)
- [ ] [Datacamp: Introduction to TensorFlow in Python](https://www.datacamp.com/courses/introduction-to-tensorflow-in-python)
- [ ] [Datacamp: Introduction to Deep Learning with Keras](https://www.datacamp.com/courses/deep-learning-with-keras-in-python)
- [ ] [Datacamp: Advanced Deep Learning with Keras](https://www.datacamp.com/courses/advanced-deep-learning-with-keras-in-python)
- [ ] [Google: Intro to Tensorflow](https://www.coursera.org/learn/intro-tensorflow)
- [ ] [Google: Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/)
- [ ] [Pluralsight: Deep Learning with Keras](https://www.pluralsight.com/courses/keras-deep-learning)
- [ ] [Udacity: Intro to TensorFlow for Deep Learning](https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187)

### Be able to implement models in PyTorch
- [ ] [Article: Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255)
- [ ] [Article: An introduction to PyTorch Lightning with comparisons to PyTorch](https://amaarora.github.io/2020/07/12/oganized-pytorch.html)
- [ ] [Article: Scaling Logistic Regression Via Multi-GPU/TPU Training](https://towardsdatascience.com/scaling-logistic-regression-for-multi-gpu-tpu-training-b4898d5049ff)
- [ ] [Article: PyTorch Lightning Bolts — From Linear, Logistic Regression on TPUs to pre-trained GANs](https://medium.com/pytorch/pytorch-lightning-bolts-from-boosted-regression-on-tpus-to-pre-trained-gans-5cebdb1f99fe)
- [ ] [Article: Keeping Up with PyTorch Lightning and Hydra](https://towardsdatascience.com/keeping-up-with-pytorch-lightning-and-hydra-31e8ed70b2cc)
- [ ] [Article: PyTorch Lightning 0.9 — synced BatchNorm, DataModules and final API!](https://medium.com/pytorch/pytorch-lightning-0-9-synced-batchnorm-datamodules-and-final-api-aae885398a9d)
- [ ] [Article: PyTorch Lightning: Metrics](https://medium.com/pytorch/pytorch-lightning-metrics-35cb5ab31857)
- [ ] [Article: PyTorch Multi-GPU Metrics Library and More in PyTorch Lightning 0.8.1](https://medium.com/pytorch/pytorch-multi-gpu-metrics-and-more-in-pytorch-lightning-0-8-1-b7cadd04893e)
- [ ] [Article: 7 Tips To Maximize PyTorch Performance](https://towardsdatascience.com/7-tips-for-squeezing-maximum-performance-from-pytorch-ca4a40951259)
- [ ] [Article: From PyTorch to PyTorch Lightning — A gentle introduction](https://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09)
- [ ] [Article: Converting From Keras To PyTorch Lightning](https://towardsdatascience.com/converting-from-keras-to-pytorch-lightning-be40326d7b7d)
- [ ] [Article: How Wadhwani AI Uses PyTorch To Empower Cotton Farmers](https://medium.com/pytorch/how-wadhwani-ai-uses-pytorch-to-empower-cotton-farmers-14397f4c9f2b)
- [ ] [Article: PyTorch Loss Functions: The Ultimate Guide](https://neptune.ai/blog/pytorch-loss-functions)
- [ ] [Article: Supercharge your Training with Pytorch Lightning + Weights & Biases](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/pytorch-lightning/Supercharge_your_Training_with_Pytorch_Lightning_%2B_Weights_%26_Biases.ipynb)
- [ ] [Article: Sharded: A New Technique To Double The Size Of PyTorch Models](https://towardsdatascience.com/sharded-a-new-technique-to-double-the-size-of-pytorch-models-3af057466dba)
- [ ] [Article: Introducing PyTorch Lightning Sharded: Train SOTA Models, With Half The Memory](https://seannaren.medium.com/introducing-pytorch-lightning-sharded-train-sota-models-with-half-the-memory-7bcc8b4484f2)
- [ ] [Article: Transform your ML-model to Pytorch with Hummingbird](https://www.maartengrootendorst.com/blog/humming/)
- [ ] [Article: Distributed model training in PyTorch using DistributedDataParallel](https://spell.ml/blog/pytorch-distributed-data-parallel-XvEaABIAAB8Ars0e)
- [ ] [Article: Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health](https://towardsdatascience.com/taming-lstms-variable-sized-mini-batches-and-why-pytorch-is-good-for-your-health-61d35642972e)
- [ ] [Article: Pad pack sequences for Pytorch batch processing with DataLoader](https://suzyahyah.github.io/pytorch/2019/07/01/DataLoader-Pad-Pack-Sequence.html)
- [ ] [Article: Understanding Bidirectional RNN in PyTorch](https://towardsdatascience.com/understanding-bidirectional-rnn-in-pytorch-5bd25a5dd66)
- [ ] [Article: PyTorch Lightning V1.2.0- DeepSpeed, Pruning, Quantization, SWA](https://medium.com/pytorch/pytorch-lightning-v1-2-0-43a032ade82b)
- [ ] [Article: But what are PyTorch DataLoaders really?](https://www.scottcondron.com/jupyter/visualisation/audio/2020/12/02/dataloaders-samplers-collate.html)
- [ ] [Article: How to Build a Streaming DataLoader with PyTorch](https://medium.com/speechmatics/how-to-build-a-streaming-dataloader-with-pytorch-a66dd891d9dd)
- [ ] [Article: Tricks for training PyTorch models to convergence more quickly](https://spell.ml/blog/pytorch-training-tricks-YAnJqBEAACkARhgD)
- [ ] [Article: The One PyTorch Trick Which You Should Know](https://towardsdatascience.com/the-one-pytorch-trick-which-you-should-know-2d5e9c1da2ca)
- [ ] [Article: A developer-friendly guide to model pruning in PyTorch](https://spell.ml/blog/model-pruning-in-pytorch-X9pXQRAAACIAcH9h)
- [ ] [Article: A developer-friendly guide to model quantization with PyTorch](https://spell.ml/blog/pytorch-quantization-X8e7wBAAACIAHPhT)
- [ ] [Article: Distributed model training in PyTorch using DistributedDataParallel](https://spell.ml/blog/pytorch-distributed-data-parallel-XvEaABIAAB8Ars0e)
- [ ] [Article: A developer-friendly guide to mixed precision training with PyTorch](https://spell.ml/blog/mixed-precision-training-with-pytorch-Xuk7YBEAACAASJam)
- [ ] [Article: Fit More and Train Faster With ZeRO via DeepSpeed and FairScale](https://huggingface.co/blog/zero-deepspeed-fairscale)
- [ ] [Article: Faster Deep Learning Training with PyTorch – a 2021 Guide](https://efficientdl.com/faster-deep-learning-in-pytorch-a-guide/)
- [ ] [Article: EINSUM IS ALL YOU NEED - EINSTEIN SUMMATION IN DEEP LEARNING](https://rockt.github.io/2018/04/30/einsum)
- [ ] [Notebook: Tensor Arithmetic](https://colab.research.google.com/github/davidbau/how-to-read-pytorch/blob/master/notebooks/1-Pytorch-Introduction.ipynb)
- [ ] [Notebook: Autograd](https://colab.research.google.com/github/davidbau/how-to-read-pytorch/blob/master/notebooks//2-Pytorch-Autograd.ipynb)
- [ ] [Notebook: Optimization](https://colab.research.google.com/github/davidbau/how-to-read-pytorch/blob/master/notebooks//3-Pytorch-Optimizers.ipynb)
- [ ] [Notebook: Network modules](https://colab.research.google.com/github/davidbau/how-to-read-pytorch/blob/master/notebooks//4-Pytorch-Modules.ipynb)
- [ ] [Notebook: Datasets and Dataloaders](https://colab.research.google.com/github/davidbau/how-to-read-pytorch/blob/master/notebooks//5-Pytorch-Dataloader.ipynb)
- [ ] [Documentation: Pytorch Lightning](https://pytorch-lightning.readthedocs.io/en/latest/)
- [ ] [Datacamp: Introduction to Deep Learning with PyTorch](https://www.datacamp.com/courses/deep-learning-with-pytorch)
- [ ] [Deeplizard: Neural Network Programming - Deep Learning with PyTorch](https://www.youtube.com/playlist?list=PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG)
- [ ] [Udacity: Intro to Deep Learning with PyTorch](https://www.udacity.com/course/deep-learning-pytorch--ud188)
- [ ] [Youtube: PyTorch Lightning 101](https://www.youtube.com/playlist?list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2)
  - [ ] [Training a classification model on MNIST with PyTorch](https://youtu.be/OMDn66kM9Qc?list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2)
  - [ ] [From PyTorch to PyTorch Lightning](https://youtu.be/DbESHcCoWbM?list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2)
  - [ ] [Lightning Data Modules](https://youtu.be/L---MBeSXFw)
  - [ ] [PyTorch Dropout, Batch size and interactive debugging](https://youtu.be/vD5iQkdqMqU)
  - [ ] [Episode 4: Implementing a PyTorch Trainer: PyTorch Lightning Trainer and callbacks under-the-hood](https://www.youtube.com/watch?v=tgp56S2eGFE)
- [ ] [Youtube: SimCLR with PyTorch Lightning](https://www.youtube.com/playlist?list=PLaMu-SDt_RB4k8VXiB3hOdsn0Y3GoXo1k)
- [ ] [Youtube: PyTorch Performance Tuning Guide](https://youtu.be/9mS1fIYj1So) `26:41:00`
- [ ] [Youtube: Skin Cancer Detection with PyTorch](https://www.youtube.com/playlist?list=PLUH_l3HbfEW0wP7ZOKUxmlnG6sntCQpHX)
    - [ ] [[PART 1] Skin Cancer Detection with PyTorch](https://www.youtube.com/watch?v=6LdS9n_L7u4) `0:10:21`
    - [ ] [[PART 2] Skin Cancer Detection with PyTorch](https://www.youtube.com/watch?v=wspdT8hRCWs) `0:21:57`
    - [ ] [[PART 3] Skin Cancer Detection with PyTorch](https://www.youtube.com/watch?v=zFqsuXs6-Us) `0:22:24`
- [ ] [Youtube: Learn with Lightning](https://www.pytorchlightning.ai/tutorials)
- [ ] [Youtube: PyTorch Tutorial - RNN & LSTM & GRU - Recurrent Neural Nets](https://www.youtube.com/watch?v=0_PgWWmauHk) `00:15:51`
- [ ] [Youtube: Pytorch Zero to All](https://www.youtube.com/playlist?list=PLlMkM4tgfjnJ3I-dbhO9JTw7gNty6o_2m)
    - [ ] [PyTorch Lecture 01: Overview](https://www.youtube.com/watch?v=SKq-pmkekTk) `0:10:18`
    - [ ] [PyTorch Lecture 02: Linear Model](https://www.youtube.com/watch?v=l-Fe9Ekxxj4) `0:12:52`
    - [ ] [PyTorch Lecture 03: Gradient Descent](https://www.youtube.com/watch?v=b4Vyma9wPHo) `0:08:24`
    - [ ] [PyTorch Lecture 04: Back-propagation and Autograd](https://www.youtube.com/watch?v=ma2KXWblllc) `0:15:25`
    - [ ] [PyTorch Lecture 05: Linear Regression in the PyTorch way](https://www.youtube.com/watch?v=113b7O3mabY) `0:11:50`
    - [ ] [PyTorch Lecture 06: Logistic Regression](https://www.youtube.com/watch?v=GAKTBQn7yKo) `0:10:41`
    - [ ] [PyTorch Lecture 07: Wide and Deep](https://www.youtube.com/watch?v=Mf8jna42p2M) `0:10:37`
    - [ ] [PyTorch Lecture 08: PyTorch DataLoader](https://www.youtube.com/watch?v=zN49HdDxHi8) `0:06:41`
    - [ ] [PyTorch Lecture 09: Softmax Classifier](https://www.youtube.com/watch?v=lvNdl7yg4Pg) `0:18:47`
    - [ ] [PyTorch Lecture 10: Basic CNN](https://www.youtube.com/watch?v=LgFNRIFxuUo) `0:15:52`
    - [ ] [PyTorch Lecture 11: Advanced CNN](https://www.youtube.com/watch?v=hqYfqNAQIjE) `0:12:58`
    - [ ] [PyTorch Lecture 12: RNN1 - Basics](https://www.youtube.com/watch?v=ogZi5oIo4fI) `0:28:47`
    - [ ] [PyTorch Lecture 13: RNN 2 - Classification](https://www.youtube.com/watch?v=1vGOQAel2yU) `0:17:22`
- [ ] [PyTorch Developer Day 2020 | Full Livestream](https://www.youtube.com/watch?v=jaPVoObpdO0)
- [ ] [Youtube: Lightning Chat: How a Grandmaster Won a Kaggle Competition Using Pytorch Lightning](https://www.youtube.com/watch?app=desktop&v=0HQCK_l-njI)

### Be able to implement models using cloud services
- [ ] [AWS: Amazon Transcribe Deep Dive: Using Feedback Loops to Improve Confidence Level of Transcription](https://www.aws.training/learningobject/video?id=27495)
- [ ] [AWS: Build a Text Classification Model with AWS Glue and Amazon SageMaker](https://www.aws.training/learningobject/video?id=27225)
- [ ] [AWS: Deep Dive on Amazon Rekognition: Building Computer Visions Based Smart Applications](https://www.aws.training/learningobject/video?id=27230)
- [ ] [AWS: Hands-on Rekognition: Automated Video Editing](https://www.aws.training/learningobject/video?id=27229)
- [ ] [AWS: Introduction to Amazon Comprehend](https://www.aws.training/learningobject/video?id=16626)
- [ ] [AWS: Introduction to Amazon Comprehend Medical](https://www.aws.training/learningobject/video?id=27159)
- [ ] [AWS: Introduction to Amazon Elastic Inference](https://www.aws.training/learningobject/video?id=27172)
- [ ] [AWS: Introduction to Amazon Forecast](https://www.aws.training/learningobject/video?id=27163)
- [ ] [AWS: Introduction to Amazon Lex](https://www.aws.training/learningobject/video?id=16516)
- [ ] [AWS: Introduction to Amazon Personalize](https://www.aws.training/learningobject/video?id=27158)
- [ ] [AWS: Introduction to Amazon Polly](https://www.aws.training/learningobject/video?id=15886)
- [ ] [AWS: Introduction to Amazon SageMaker Ground Truth](https://www.aws.training/learningobject/video?id=27162)
- [ ] [AWS: Introduction to Amazon SageMaker Neo](https://www.aws.training/learningobject/video?id=27160)
- [ ] [AWS: Introduction to Amazon Transcribe](https://www.aws.training/learningobject/video?id=19443)
- [ ] [AWS: Introduction to Amazon Translate](https://www.aws.training/learningobject/video?id=19442)
- [ ] [AWS: Introduction to AWS Marketplace - Machine Learning Category](https://www.aws.training/learningobject/video?id=27165)
- [ ] [AWS: Machine Learning Exam Basics](https://www.aws.training/learningobject/curriculum?id=27271)
- [ ] [AWS: Neural Machine Translation with Sockeye](https://www.aws.training/learningobject/video?id=27236)
- [ ] [AWS: Process Model: CRISP-DM on the AWS Stack](https://www.aws.training/learningobject/wbc?id=27200)
- [ ] [AWS: Satellite Image Classification in SageMaker](https://www.aws.training/learningobject/video?id=27231)
- [ ] [Datacamp: Introduction to AWS Boto in Python](https://www.datacamp.com/courses/introduction-to-aws-boto-in-python)
- [ ] [edX: Amazon SageMaker: Simplifying Machine Learning Application Development](https://www.edx.org/course/simplifying-machine-learning-app-development-with-amazon-sagemaker)

### Be able to apply unsupervised learning algorithms
- [ ] [Article: From Research to Production with Deep Semi-Supervised Learning](https://towardsdatascience.com/from-research-to-production-with-deep-semi-supervised-learning-7caaedc39093)
- [ ] [Article: RecSys 2020 - Takeaways and Notable Papers](https://eugeneyan.com/writing/recsys2020/)
- [ ] [Article: Paper Summary: DeViSE: A Deep Visual-Semantic Embedding Model](https://medium.com/@hyponymous/paper-summary-devise-a-deep-visual-semantic-embedding-model-c5f308d5ff98)
- [ ] [Article: An overview of proxy-label approaches for semi-supervised learning](https://ruder.io/semi-supervised/)
- [ ] [Article: Create, Visualize and Interpret Customer Segments](https://www.maartengrootendorst.com/blog/customer/)
- [ ] [Article: A gentle introduction to HDBSCAN and density-based clustering](https://towardsdatascience.com/a-gentle-introduction-to-hdbscan-and-density-based-clustering-5fd79329c1e8)
- [ ] [Article: Grouping data points with k-means clustering](https://www.jeremyjordan.me/grouping-data-points-with-k-means-clustering/)
- [ ] [Article: Soft clustering with Gaussian mixed models (EM)](https://www.jeremyjordan.me/gaussian-mixed-models/)
- [ ] [Article: Introduction to autoencoders](https://www.jeremyjordan.me/autoencoders/)
- [ ] [Article: Variational autoencoders](https://www.jeremyjordan.me/variational-autoencoders/)
- [ ] [Article: Principal components analysis (PCA)](https://www.jeremyjordan.me/principal-components-analysis/)
- [ ] [Article: Deep Inside Autoencoders](https://nathanhubens.github.io/posts/deep%20learning/2018/02/25/deep-inside-autoencoders.html)
- [ ] [Article: Build a simple Image Retrieval System with an Autoencoder](https://nathanhubens.github.io/posts/deep%20learning/2018/08/24/image-retrieval.html)
- [ ] [Article: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://medium.com/@nainaakash012/unsupervised-learning-of-visual-features-by-contrasting-cluster-assignments-fbedc8b9c3db)
- [ ] [Article: A Framework For Contrastive Self-Supervised Learning And Designing A New Approach](https://towardsdatascience.com/a-framework-for-contrastive-self-supervised-learning-and-designing-a-new-approach-3caab5d29619)
- [ ] [Article: Self-supervised learning: The dark matter of intelligence](https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence)
- [ ] [Article: Contrastive Self-Supervised Learning](https://ankeshanand.com/blog/2020/01/26/contrative-self-supervised-learning.html)
- [ ] [Article: Understanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL)](https://untitled-ai.github.io/understanding-self-supervised-contrastive-learning.html)
- [ ] [Article: Affinity Propagation Algorithm Explained](https://towardsdatascience.com/unsupervised-machine-learning-affinity-propagation-algorithm-explained-d1fef85f22c8)
- [ ] [Article: Algorithm Breakdown: Affinity Propagation](https://www.ritchievink.com/blog/2018/05/18/algorithm-breakdown-affinity-propagation/)
- [ ] [Article: From Autoencoder to Beta-VAE](https://lilianweng.github.io/lil-log/2018/08/12/from-autoencoder-to-beta-vae.html)
- [ ] [Article: Self-Supervised Representation Learning](https://lilianweng.github.io/lil-log/2019/11/10/self-supervised-learning.html)
- [ ] [Article: GANs in computer vision - Introduction to generative learning](https://theaisummer.com/gan-computer-vision/)
- [ ] [Article: GANs in computer vision - self-supervised adversarial training and high-resolution image synthesis with style incorporation](https://theaisummer.com/gan-computer-vision-style-gan/)
- [ ] [Article: GANs in computer vision - semantic image synthesis and learning a generative model from a single image](https://theaisummer.com/gan-computer-vision-semantic-synthesis/)
- [ ] [Article: GANs in computer vision - Improved training with Wasserstein distance, game theory control and progressively growing schemes](https://theaisummer.com/gan-computer-vision-incremental-training/)
- [ ] [Article: GANs in computer vision - Conditional image synthesis and 3D object generation](https://theaisummer.com/gan-computer-vision-object-generation/)
- [ ] [Article: Decrypt Generative Adversarial Networks (GAN)](https://theaisummer.com/Generative_Artificial_Intelligence/)
- [ ] [Article: How to Generate Images using Autoencoders](https://theaisummer.com/Autoencoder/)
- [ ] [Article: Deepfakes: Face synthesis with GANs and Autoencoders](https://theaisummer.com/deepfakes/)
- [ ] [Article: EfficientDet Meets Pytorch-Lightning](https://medium.com/@YassineAlouini/efficientdet-meets-pytroch-lightning-6f1dcf3b73bd)
- [ ] [Berkeley: Deep Unsupervised Learning Spring 2020](https://www.youtube.com/playlist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP)
	- [ ] [L1 Introduction -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020](https://www.youtube.com/watch?v=V9Roouqfu-M) `1:10:02`
	- [ ] [L2 Autoregressive Models -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020](https://www.youtube.com/watch?v=iyEOk8KCRUw) `2:27:23`
	- [ ] [L3 Flow Models -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley -- Spring 2020](https://www.youtube.com/watch?v=JBb5sSC0JoY) `1:56:53`
	- [ ] [L4 Latent Variable Models (VAE) -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley](https://www.youtube.com/watch?v=FMuvUZXMzKM) `2:19:33`
	- [ ] [Lecture 5 Implicit Models -- GANs  Part I --- UC Berkeley, Spring 2020](https://www.youtube.com/watch?v=1CT-kxjYbFU) `2:32:32`
	- [ ] [Lecture 6  Implicit Models / GANs part II --- CS294-158-SP20 Deep Unsupervised Learning -- Berkeley](https://www.youtube.com/watch?v=0W1dixJfKL4) `2:09:14`
	- [ ] [Lecture 7 Self-Supervised Learning -- UC Berkeley Spring 2020 - CS294-158 Deep Unsupervised Learning](https://www.youtube.com/watch?v=dMUes74-nYY) `2:20:41`
	- [ ] [L8 Round-up of Strengths and Weaknesses of Unsupervised Learning Methods -- UC Berkeley SP20](https://www.youtube.com/watch?v=1sJuWg5dULg) `0:41:51`
	- [ ] [L9 Semi-Supervised Learning and Unsupervised Distribution Alignment -- CS294-158-SP20 UC Berkeley](https://www.youtube.com/watch?v=PXOhi6m09bA) `2:16:00`
	- [ ] [L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning](https://www.youtube.com/watch?v=pPyOlGvWoXA) `3:09:49`
	- [ ] [L11 Language Models -- guest instructor: Alec Radford (OpenAI) --- Deep Unsupervised Learning SP20](https://www.youtube.com/watch?v=BnpB3GrpsfM) `2:38:19`
	- [ ] [L12 Representation Learning for Reinforcement Learning --- CS294-158 UC Berkeley Spring 2020](https://www.youtube.com/watch?v=YqvhDPd1UEw) `2:01:56`
- [ ] [Datacamp: Customer Segmentation in Python](https://www.datacamp.com/courses/customer-segmentation-in-python)
- [ ] [Datacamp: Unsupervised Learning in Python](https://www.datacamp.com/courses/unsupervised-learning-in-python)
- [ ] [Deck: Demystifying Self-Supervised Learning for Visual Recognition](https://docs.google.com/presentation/d/14CjZoX1V--XrIKAlpCFpIsHPjtV_P-OxObRdR5vVljY/edit#slide=id.g94fcc32373_0_58)
- [ ] [DeepMind: Inefficient Data Efficiency](https://www.facebook.com/wdeepvision2020/videos/893497114486588/)
- [ ] [Google: Clustering](https://developers.google.com/machine-learning/clustering)
- [ ] [Udacity: Segmentation and Clustering](https://www.udacity.com/course/segmentation-and-clustering--ud981)
- [ ] [Wandb: Unsupervised Visual Representation Learning with SwAV](https://app.wandb.ai/authors/swav-tf/reports/Unsupervised-Visual-Representation-Learning-with-SwAV--VmlldzoyMjg3Mzg)
- [ ] [Youtube: BYOL: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Paper Explained)](https://www.youtube.com/watch?v=YPfUiOMYOEE&feature=youtu.be)
- [ ] [Youtube: A critical analysis of self-supervision, or what we can learn from a single image (Paper Explained)](https://youtu.be/l5he9JNJqHA)
- [ ] [Youtube: Week 10 – Lecture: Self-supervised learning (SSL) in computer vision (CV)](https://www.youtube.com/watch?v=0KeR6i1_56g&feature=youtu.be)
- [ ] [Youtube: CVPR 2020 Tutorial: Towards Annotation-Efficient Learning](https://youtu.be/MaGudzppu3I)
- [ ] [Youtube: Yuki Asano | Self-Supervision | Self-Labelling | Labelling Unlabelled videos | CV | CTDS.Show #81](https://youtu.be/LPdbnasJ9wI)
- [ ] [Youtube: Contrastive Clustering with SwAV](https://youtu.be/jCg97EAVsy8)
- [ ] [Youtube: Variational Autoencoders - EXPLAINED!](https://www.youtube.com/watch?v=fcvYpzHmhvA) `0:17:36`
- [ ] [Youtube: OptaProAnalyticsForum– Learning to watch football: Self-supervised representations for tracking data](https://youtu.be/H1iho17lnoI)
- [ ] [Youtube: Can a Neural Net tell if an image is mirrored? – Visual Chirality](https://youtu.be/rbg1Mdo2LZM)
- [ ] [Youtube: Deep InfoMax: Learning deep representations by mutual information estimation and maximization](https://www.youtube.com/watch?v=o1HIkn8LEsw)
- [ ] [Deep Learning Lecture Summer 2020](https://www.youtube.com/playlist?list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj)
    - [ ] [Deep Learning: Unsupervised Learning - Part 1](https://www.youtube.com/watch?v=aoOE4bJxybA&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=47&t=0s)
    - [ ] [Deep Learning: Unsupervised Learning - Part 2](https://www.youtube.com/watch?v=GpAHm7dvP_k&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=48&t=0s)
    - [ ] [Deep Learning: Unsupervised Learning - Part 3](https://www.youtube.com/watch?v=fXO1fOXnOTI&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=49&t=0s)
    - [ ] [Deep Learning: Unsupervised Learning - Part 4](https://www.youtube.com/watch?v=K27a_doRoxw&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=50&t=0s)
    - [ ] [Deep Learning: Unsupervised Learning - Part 5](https://www.youtube.com/watch?v=4Ot22wkEdfU&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=51&t=0s)
    - [ ] [Deep Learning: Weakly and Self-Supervised Learning - Part 1](https://www.youtube.com/watch?v=Vj_JeSZG1EA&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=57&t=0s)
    - [ ] [Deep Learning: Weakly and Self-Supervised Learning - Part 2](https://www.youtube.com/watch?v=KjcSfpLin7U&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=58&t=0s)
    - [ ] [Deep Learning: Weakly and Self-Supervised Learning - Part 3](https://www.youtube.com/watch?v=EqMwbP7Smxg&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=59&t=0s)
    - [ ] [Deep Learning: Weakly and Self-Supervised Learning - Part 4](https://www.youtube.com/watch?v=zIDdTstAqWU&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=60&t=0s)
- [ ] [ECCV 2020: New Frontiers for Learning with Limited Labels or Data](https://nvlabs.github.io/eccv2020-limited-labels-data-tutorial/)
    - [ ] [Introduction to New Frontiers on Learning with Limited Labels or Data](https://youtu.be/lHsLUYk80z4)
    - [ ] [Self-Supervised Part and Viewpoint Discovery from Image Collections](https://youtu.be/5kzU6NkvGX4)
    - [ ] [Learning Visual Correspondences across Instances and Video Frames](https://youtu.be/_Sug0ICzKlk)
    - [ ] [Limitless Labels in a Labelless World: Weak Supervision with Noisy Labels](https://youtu.be/UtxQkIoei0o)
    - [ ] [Inverting Neural Networks for Data-free Knowledge Transfer](https://youtu.be/ddEtea4ntEU)
    - [ ] [Learning Efficiently with Biologically Inspired Feedback](https://youtu.be/8N9AF8V52-E)
- [ ] [Youtube: Self-Supervised Learning - What is Next? - Workshop at ECCV 2020, August 28th](https://www.youtube.com/playlist?list=PL53R9Jy9Cc0zdv9OqvJ5YsZH2-AMKo9gM)
    - [ ] [Next Challenges for Self-Supervised Learning - Aäron van den Oord](https://www.youtube.com/watch?v=jJozjCG8Cqs) `0:20:13`
    - [ ] [Perspectives on Unsupervised Representation Learning - Paolo Favaro](https://www.youtube.com/watch?v=APwHDZZcLuY) `0:42:41`
    - [ ] [Learning and Transferring Visual Representations with Few Labels - Carl Doersch](https://www.youtube.com/watch?v=RWCc0nZOSBw) `0:32:53`
    - [ ] [Multi-view Invariance and Grouping for Self-Supervised Learning - Ishan Misra](https://www.youtube.com/watch?v=gbziPIn9uDI) `0:36:31`
    - [ ] [Representation Learning beyond Instance Discrimination and Semantic Categorization - Stella Yu](https://www.youtube.com/watch?v=F5mt4z-w_Mk) `0:43:09`
    - [ ] [Self-Supervision as a Path to a Post-Dataset Era - Alexei Alyosha Efros](https://www.youtube.com/watch?v=iTbfEXFwDJc) `0:38:06`
    - [ ] [Self-Supervision & Modularity: Cornerstones for Generalization in Embodied Agents - Deepak Pathak](https://www.youtube.com/watch?v=fUMpC_hoedA) `0:41:56`
- [ ] [Youtube: Marco Cuturi - A Primer on Optimal Transport](https://youtu.be/6iR1E6t1MMQ)
- [ ] [Youtube: Sebastian Ruder: Neural Semi-supervised Learning under Domain Shift](https://www.youtube.com/watch?v=tpAr5-Y4JxU)
- [ ] [Youtube: Clustering Algorithms](https://www.youtube.com/playlist?list=PLBv09BD7ez_6lYVoZ1RzVcOPIT5Lfjo0Y)
- [ ] [Youtube: Beyond supervised learning](https://www.youtube.com/watch?v=lFWJB1ig0e0)

### Be able to implement NLP models
- [ ] [Article: Understanding ARPA and Language Models](https://medium.com/@canadaduane/understanding-arpa-and-language-models-115d6cbc3893)
- [ ] [Article: Transformer-based Encoder-Decoder Models](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Encoder_Decoder_Model.ipynb)
- [ ] [Article: Zero-Shot Learning in Modern NLP](https://joeddav.github.io/blog/2020/05/29/ZSL.html)
- [ ] [Article: Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/)
- [ ] [Article: Text Data Cleanup - Dynamic Embedding Visualisation](https://www.ntentional.com/nlp/visualization/bokeh/clustering/2020/06/29/Text-Cleaning-With-Clustering.html)
- [ ] [Article: The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)](https://jalammar.github.io/illustrated-bert/)
- [ ] [Article: Intuition & Use-Cases of Embeddings in NLP & beyond](https://jalammar.github.io/skipgram-recommender-talk/)
- [ ] [Article: The Illustrated GPT-2 (Visualizing Transformer Language Models)](https://jalammar.github.io/illustrated-gpt2/)
- [ ] [Article: The Illustrated Word2vec](https://jalammar.github.io/illustrated-word2vec/)
- [ ] [Article: All Our N-gram are Belong to You](https://ai.googleblog.com/2006/08/all-our-n-gram-are-belong-to-you.html)
- [ ] [Article: Introducing spaCy](https://explosion.ai/blog/introducing-spacy)
- [ ] [Article: DialogRPT with Huggingface Transformers](https://parthplc.medium.com/dialogrpt-with-huggingface-transformers-which-comments-get-more-likes-more-replies-and-are-5e7e13a5429f)
- [ ] [Article: How to Outperform GPT-3 by Combining Task Descriptions With Supervised Learning](http://www.timoschick.com/explanatory%20notes/2020/10/23/pattern-exploiting-training.html)
- [ ] [Article: How we used Universal Sentence Encoder and FAISS to make our search 10x smarter](https://blog.onebar.io/building-a-semantic-search-engine-using-open-source-components-e15af5ed7885)
- [ ] [Article: Porting fairseq wmt19 translation system to transformers](https://huggingface.co/blog/porting-fsmt)
- [ ] [Article: NLP: Pre-trained Sentiment Analysis](https://medium.com/@b.terryjack/nlp-pre-trained-sentiment-analysis-1eb52a9d742c)
- [ ] [Article: How to build a State-of-the-Art Conversational AI with Transfer Learning](https://medium.com/huggingface/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2d818ac26313)
- [ ] [Article: The Current Best of Universal Word Embeddings and Sentence Embeddings](https://medium.com/huggingface/universal-word-sentence-embeddings-ce48ddc8fc3a)
- [ ] [Article: Long Short-Term Memory: From Zero to Hero with PyTorch](https://blog.floydhub.com/long-short-term-memory-from-zero-to-hero-with-pytorch/)
- [ ] [Article: Ten trends in Deep learning NLP](https://blog.floydhub.com/ten-trends-in-deep-learning-nlp)
- [ ] [Article: Google mT5 multilingual text-to-text transformer: A Brief Paper Analysis](https://medium.com/ai-in-plain-english/google-mt5-multilingual-text-to-text-transformer-a-brief-paper-analysis-30591a3cb7d5)
- [ ] [Article: String Matching with BERT, TF-IDF, and more!](https://www.maartengrootendorst.com/blog/polyfuzz/)
- [ ] [Article: Keyword Extraction with BERT](https://www.maartengrootendorst.com/blog/keybert/)
- [ ] [Article: Creating a class-based TF-IDF with Scikit-Learn](https://www.maartengrootendorst.com/blog/ctfidf/)
- [ ] [Article: Topic Modeling with BERT](https://www.maartengrootendorst.com/blog/bertopic/)
- [ ] [Article: NLP Year In Review](https://pakodas.substack.com/p/nlp-year-in-review)
- [ ] [Article: Using an NLP Q&A System To Study Climate Hazards and Nature-Based Solutions](https://medium.com/omdena/using-an-nlp-q-a-system-to-study-climate-hazards-and-nature-based-solutions-c191920766d7)
- [ ] [Article: Building a sentence embedding index with fastText and BM25](https://towardsdatascience.com/building-a-sentence-embedding-index-with-fasttext-and-bm25-f07e7148d240)
- [ ] [Article: ML and NLP Research Highlights of 2020](https://ruder.io/research-highlights-2020/)
- [ ] [Article: Hugging Face Reads - 01/2021 - Sparsity and Pruning](https://discuss.huggingface.co/t/hugging-face-reads-01-2021-sparsity-and-pruning/3144)
- [ ] [Article: Recent Advances in Language Model Fine-tuning](https://ruder.io/recent-advances-lm-fine-tuning/)
- [ ] [Article: Understanding building blocks of ULMFIT](https://medium.com/mlreview/understanding-building-blocks-of-ulmfit-818d3775325b)
- [ ] [Article: Swiss army knife for unsupervised task solving](https://towardsdatascience.com/swiss-army-knife-for-unsupervised-task-solving-26f9acf7c023)
- [ ] [Article: Multi-Label Text Classification](https://radiant-brushlands-42789.herokuapp.com/towardsdatascience.com/multi-label-text-classification-5c505fdedca8)
- [ ] [Article: Key topics extraction and contextual sentiment of users reviews](https://tech.goibibo.com/key-topics-extraction-and-contextual-sentiment-of-users-reviews-20e63c0fd7ca)
- [ ] [Article: Understanding Convolutional Neural Networks for NLP](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/)
- [ ] [Article: Attention and Memory in Deep Learning and NLP](http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/)
- [ ] [Article: Unsupervised synonym harvesting](https://towardsdatascience.com/unsupervised-synonym-harvesting-d592eaaf3c15)
- [ ] [Article: Hugging Face Reads, Feb. 2021 - Long-range Transformers](https://huggingface.co/blog/long-range-transformers)
- [ ] [Article: Maximizing BERT model performance](https://towardsdatascience.com/maximizing-bert-model-performance-539c762132ab)
- [ ] [Article: Unsupervised creation of interpretable sentence representations](https://towardsdatascience.com/unsupervised-creation-of-interpretable-sentence-representations-851e74921cf9)
- [ ] [Article: Document search with fragment embeddings](https://towardsdatascience.com/document-search-with-fragment-embeddings-7e1d73eb0104)
- [ ] [Article: Unsupervised NER using BERT](https://towardsdatascience.com/unsupervised-ner-using-bert-2d7af5f90b8a)
- [ ] [Article: Examining BERT’s raw embeddings](https://towardsdatascience.com/examining-berts-raw-embeddings-fd905cb22df7)
- [ ] [Article: T5 — a model that explores the limits of transfer learning](https://towardsdatascience.com/t5-a-model-that-explores-the-limits-of-transfer-learning-fb29844890b7)
- [ ] [Article: T5 — XLNet — a clever language modeling solution](https://towardsdatascience.com/xlnet-a-clever-language-modeling-solution-ab41e87798b0)
- [ ] [Article: A review of BERT based models](https://towardsdatascience.com/a-review-of-bert-based-models-4ffdc0f15d58)
- [ ] [Article: Deconstructing BERT](https://towardsdatascience.com/deconstructing-bert-reveals-clues-to-its-state-of-art-performance-in-nlp-tasks-76a7e828c0f1)
- [ ] [Article: Text classification from few training examples](https://maelfabien.github.io/machinelearning/NLP_5/)
- [ ] [Article: Improved Few-Shot Text classification](https://maelfabien.github.io/machinelearning/NLP_6/)
- [ ] [Article: Solving NER with BERT for any entity type with very little training data (compared to past approaches)](https://ajitrajasekharan.medium.com/solving-ner-with-bert-for-any-entity-type-with-very-little-training-data-compared-to-past-50094e2e3df9)
- [ ] [Article: Trends in input representation for state-of-art NLP models (2019)](https://ajitrajasekharan.medium.com/trends-in-input-representation-for-state-of-art-nlp-models-2019-fd162ebccd15)
- [ ] [Article: Brief review of word embedding families](https://medium.com/analytics-vidhya/brief-review-of-word-embedding-families-2019-b2bbc601bbfe)
- [ ] [Article: GPT-2 A nascent transfer learning method that could eliminate supervised learning in some NLP tasks](https://ajitrajasekharan.medium.com/gpt-2-a-promising-but-nascent-transfer-learning-method-that-could-reduce-or-even-eliminate-in-some-48ea3370cc21)
- [ ] [Article: Semantic search using BERT embeddings](https://ajitrajasekharan.medium.com/semantic-search-using-bert-embeddings-511c5d78348e)
- [ ] [Article: A Survey of Long-Term Context in Transformers](https://www.pragmatic.ml/a-survey-of-methods-for-incorporating-long-term-context/)
- [ ] [Article: A Deep Dive into the Reformer](https://www.pragmatic.ml/reformer-deep-dive/)
- [ ] [Article: Representation Learning and Retrieval](https://www.pragmatic.ml/language-modeling-and-retrieval/)
- [ ] [Article: Large Memory Layers with Product Keys](https://www.pragmatic.ml/large-memory-layers-with-product-keys/)
- [ ] [Article: Pattern-Exploiting Training](https://www.pragmatic.ml/pet/)
- [ ] [Article: Optimal Transport and the Sinkhorn Transformer](https://www.pragmatic.ml/sparse-sinkhorn-attention/)
- [ ] [Article: Talking-Heads Attention](https://www.pragmatic.ml/talking-heads-attention/)
- [ ] [Article: Rebuilding the most popular spellchecker. Part 1](https://zverok.github.io/blog/2021-01-05-spellchecker-1.html)
- [ ] [Article: Rebuilding the spellchecker, pt.2: Just look in the dictionary, they said!](https://zverok.github.io/blog/2021-01-09-spellchecker-2.html)
- [ ] [Article: Rebuilding the spellchecker, pt.3: Lookup—compounds and solutions](https://zverok.github.io/blog/2021-01-14-spellchecker-3.html)
- [ ] [Article: Rebuilding the spellchecker, pt.4: Introduction to suggest algorithm](https://zverok.github.io/blog/2021-01-21-spellchecker-4.html)
- [ ] [Article: Rebuilding the spellchecker: Hunspell and the order of edits](https://zverok.github.io/blog/2021-01-28-spellchecker-5.html)
- [ ] [Article: Performers: The Kernel Trick, Random Fourier Features, and Attention](https://teddykoker.com/2020/11/performers/)
- [ ] [Article: Advance BERT model via transferring knowledge from Cross-Encoders to Bi-Encoders](https://towardsdatascience.com/advance-nlp-model-via-transferring-knowledge-from-cross-encoders-to-bi-encoders-3e0fc564f554)
- [ ] [Article: Interactive Topic Modeling with BERTopic](https://towardsdatascience.com/interactive-topic-modeling-with-bertopic-1ea55e7d73d8)
- [ ] [Article: Commonsense Reasoning for Natural Language Processing](http://veredshwartz.blogspot.com/2021/01/commonsense-reasoning-for-natural.html)
- [ ] [Article: Language Models](http://veredshwartz.blogspot.com/2015/09/language-models.html)
- [ ] [Article: Zero shot NER using RoBERTA](https://krishansubudhi.github.io/deeplearning/2020/06/25/ROBERTA-zeroshot-NER.html)
- [ ] [Article: Understanding Climate Change Domains through Topic Modeling](https://omdena.com/blog/topic-modeling/)
- [ ] [Article: Simple PyTorch Transformer Example with Greedy Decoding](https://colab.research.google.com/drive/1swXWW5sOLW8zSZBaQBYcGQkQ_Bje_bmI)
- [ ] [Article: Topic Modeling for Keyword Extraction](http://bugra.github.io/posts/2017/2/5/topic-modeling-for-keyword-extraction/)
- [ ] [Article: Poor man’s GPT-3: Few shot text generation with T5 Transformer](https://towardsdatascience.com/poor-mans-gpt-3-few-shot-text-generation-with-t5-transformer-51f1b01f843e)
- [ ] [Article: Paraphrasing](http://veredshwartz.blogspot.com/2017/08/paraphrasing.html)
- [ ] [Article: Spelling Correction: How to make an accurate and fast corrector](https://towardsdatascience.com/spelling-correction-how-to-make-an-accurate-and-fast-corrector-dc6d0bcbba5f)
- [ ] [Article: Text Generation](http://veredshwartz.blogspot.com/2019/08/text-generation.html)
- [ ] [Article: How to steal modern NLP systems with gibberish?](http://www.cleverhans.io/2020/04/06/stealing-bert.html)
- [ ] [Article: Part of Speech Tagging with Hidden Markov Chain Models](https://towardsdatascience.com/part-of-speech-tagging-with-hidden-markov-chain-models-e9fccc835c0e)
- [ ] [Article: Building RNNs is Fun with PyTorch and Google Colab](https://medium.com/dair-ai/building-rnns-is-fun-with-pytorch-and-google-colab-3903ea9a3a79)
- [ ] [Article: Automatically Summarize Trump’s State of the Union Address](https://towardsdatascience.com/automatically-summarize-trumps-state-of-the-union-address-6757c6af6534)
- [ ] [Article: Evaluation Metrics for Language Modeling](https://thegradient.pub/understanding-evaluation-metrics-for-language-models/)
- [ ] [Article: Does GPT-2 Know Your Phone Number?](https://bair.berkeley.edu/blog/2020/12/20/lmmem/)
- [ ] [Article: ColumnTransformer Meets Natural Language Processing](https://towardsdatascience.com/columntransformer-meets-natural-language-processing-da1f116dd69f)
- [ ] [Article: pyLDAvis: Topic Modelling Exploration Tool That Every NLP Data Scientist Should Know](https://neptune.ai/blog/pyldavis-topic-modelling-exploration-tool-that-every-nlp-data-scientist-should-know)
- [ ] [Article: Explain NLP models with LIME & SHAP](https://towardsdatascience.com/explain-nlp-models-with-lime-shap-5c5a9f84d59b)
- [ ] [Article: When Topic Modeling is Part of the Text Pre-processing](https://towardsdatascience.com/when-topic-modeling-is-part-of-the-text-pre-processing-294b58d35514)
- [ ] [Article: Automatic Topic Labeling in 2018: History and Trends](https://medium.com/datadriveninvestor/automatic-topic-labeling-in-2018-history-and-trends-29c128cec17)
- [ ] [Article: The Annotated Transformer](https://nlp.seas.harvard.edu/2018/04/03/attention.html)
- [ ] [Article: Question Classification using Self-Attention Transformer — Part 1](https://medium.com/nerd-for-tech/question-classification-using-self-attention-transformer-part-1-33e990636e76)
- [ ] [Article: Question Classification using Self-Attention Transformer — Part 1.1](https://medium.com/nerd-for-tech/question-classification-using-self-attention-transformer-part-1-1-3b4224cd4757)
- [ ] [Article: Question Classification using Self-Attention Transformer — Part 2](https://medium.com/nerd-for-tech/question-classification-using-self-attention-transformer-part-2-910b89c7116a)
- [ ] [Article: Question Classification using Self-Attention Transformer — Part 3](https://medium.com/nerd-for-tech/question-classification-using-self-attention-transformer-part-3-74efbda22451)
- [ ] [Article: Attention? An Other Perspective!: Part 1](https://learningturtle.github.io/Blog/posts/attention_another_perspective/)
- [ ] [Article: Attention? An Other Perspective!: Part 2](https://learningturtle.github.io/Blog/posts/attention_another_perspective_part2/)
- [ ] [Article: Attention? An Other Perspective!: Part 3](https://learningturtle.github.io/Blog/posts/attention_another_perspective_part3/)
- [ ] [Article: Attention? An Other Perspective!: Part 4](https://learningturtle.github.io/Blog/posts/attention_another_perspective_part4/)
- [ ] [Article: Attention? An Other Perspective!: Part 5](https://learningturtle.github.io/Blog/posts/attention_another_perspective_part5/)
- [ ] [Article: On word embeddings - Part 1](https://ruder.io/word-embeddings-1/)
- [ ] [Article: On word embeddings - Part 2: Approximating the Softmax](https://ruder.io/word-embeddings-softmax/)
- [ ] [Article: On word embeddings - Part 3: The secret ingredients of word2vec](https://ruder.io/secret-word2vec/)
- [ ] [Article: A survey of cross-lingual word embedding models](https://ruder.io/cross-lingual-embeddings/)
- [ ] [Article: Leveraging Pre-trained Language Model Checkpoints for Encoder-Decoder Models](https://huggingface.co/blog/warm-starting-encoder-decoder)
- [ ] [Article: Semantic search using BERT embeddings](https://medium.com/@ajitrajasekharan/semantic-search-using-bert-embeddings-511c5d78348e)
- [ ] [Article: GPT-2 A nascent transfer learning method that could eliminate supervised learning in some NLP tasks](https://medium.com/@ajitrajasekharan/gpt-2-a-promising-but-nascent-transfer-learning-method-that-could-reduce-or-even-eliminate-in-some-48ea3370cc21)
- [ ] [Article: Brief review of word embedding families (2019)](https://medium.com/analytics-vidhya/brief-review-of-word-embedding-families-2019-b2bbc601bbfe)
- [ ] [Article: Trends in input representation for state-of-art NLP models (2019)](https://medium.com/@ajitrajasekharan/trends-in-input-representation-for-state-of-art-nlp-models-2019-fd162ebccd15)
- [ ] [Article: An Overview of Multi-Task Learning in Deep Neural Networks](https://ruder.io/multi-task/)
- [ ] [Article: Deep Learning for NLP Best Practices](https://ruder.io/deep-learning-nlp-best-practices/)
- [ ] [Article: Learning to select data for transfer learning](https://ruder.io/learning-select-data/)
- [ ] [Article: Multi-Task Learning Objectives for Natural Language Processing](https://ruder.io/multi-task-learning-nlp/)
- [ ] [Article: Word embeddings in 2017: Trends and future direction](https://ruder.io/word-embeddings-2017/)
- [ ] [Article: Tracking the Progress in Natural Language Processing](https://ruder.io/tracking-progress-nlp/)
- [ ] [Article: NLP's ImageNet moment has arrived](https://ruder.io/nlp-imagenet/)
- [ ] [Article: A Review of the Neural History of Natural Language Processing](https://ruder.io/a-review-of-the-recent-history-of-nlp/)
- [ ] [Article: 10 Exciting Ideas of 2018 in NLP](https://ruder.io/10-exciting-ideas-of-2018-in-nlp/)
- [ ] [Article: The 4 Biggest Open Problems in NLP](https://ruder.io/4-biggest-open-problems-in-nlp/)
- [ ] [Article: Neural Transfer Learning for Natural Language Processing](https://ruder.io/thesis/)
- [ ] [Article: The State of Transfer Learning in NLP](https://ruder.io/state-of-transfer-learning-in-nlp/)
- [ ] [Article: Unsupervised Cross-lingual Representation Learning](https://ruder.io/unsupervised-cross-lingual-learning/)
- [ ] [Article: Why You Should Do NLP Beyond English](https://ruder.io/nlp-beyond-english/)
- [ ] [Article: A review of BERT based models](https://towardsdatascience.com/a-review-of-bert-based-models-4ffdc0f15d58)
- [ ] [Article: Character level language model RNN](https://arig23498.github.io/char-level-language-model)
- [ ] [Article: How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning](https://towardsdatascience.com/semantic-code-search-3cd6d244a39c)
- [ ] [Article: How To Create Data Products That Are Magical Using Sequence-to-Sequence Models](https://towardsdatascience.com/how-to-create-data-products-that-are-magical-using-sequence-to-sequence-models-703f86a231f8)
- [ ] [Article: State-of-the-Art Language Models in 2020](https://jinglescode.github.io/2020/05/28/state-of-the-art-language-models-2020/)
- [ ] [Article: UNDERSTANDING WORD2VEC THROUGH CULTURAL DIMENSIONS](https://cameronraymond.me/blog/word2vec-cultural-dims/)
- [ ] [Article: How to solve 90% of NLP problems: a step-by-step guide](https://mlpowered.com/posts/how-to-solve-90-nlp/)
- [ ] [Article: The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
- [ ] [Article: How to Apply BERT to Arabic and Other Languages](https://mccormickml.com/2020/10/05/multilingual-bert/)
- [ ] [Article: The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)
- [ ] [Article: Under the Hood of RNNs](https://wandb.ai/authors/rnn-viz/reports/Under-the-Hood-of-RNNs--VmlldzoyNTQ4MjY)
- [ ] [Article: Gaussian Mixture Models for Clustering](https://towardsdatascience.com/gaussian-mixture-models-for-clustering-3f62d0da675)
- [ ] [Article: What Semantic Search Can do for You](https://medium.com/deepset-ai/what-semantic-search-can-do-for-you-ea5b1e8dfa7f)
- [ ] [Article: The Annotated GPT-2](https://amaarora.github.io/2020/02/18/annotatedGPT2.html)
- [ ] [Article: How to Use n-gram Models to Detect Format Errors in Datasets](https://towardsdatascience.com/how-to-use-n-gram-models-to-detect-format-errors-in-datasets-bb2171842499)
- [ ] [Article: How to Use n-gram Models to Detect Format Errors in Datasets](https://towardsdatascience.com/how-to-use-n-gram-models-to-detect-format-errors-in-datasets-bb2171842499)
- [ ] [Article: Shrinking fastText embeddings so that it fits Google Colab](https://medium.com/@vasnetsov93/shrinking-fasttext-embeddings-so-that-it-fits-google-colab-cd59ab75959e)
- [ ] [Article: Perplexity Intuition (and its derivation)](https://towardsdatascience.com/perplexity-intuition-and-derivation-105dd481c8f3)
- [ ] [Article: How To Do Things With Words. And Counters](https://nbviewer.jupyter.org/url/norvig.com/ipython/How%20to%20Do%20Things%20with%20Words.ipynb)
- [ ] [Article: ML and NLP Publications in 2020](https://www.marekrei.com/blog/ml-and-nlp-publications-in-2020/)
- [ ] [Article: How GPT3 Works](https://jalammar.github.io/how-gpt3-works-visualizations-animations/)
- [ ] [Article: Explaining RNNs without neural networks](https://explained.ai/rnn/index.html)
- [ ] [Article: Implementing Bengio’s Neural Probabilistic Language Model (NPLM) using Pytorch](https://abhinavcreed13.github.io/blog/bengio-trigram-nplm-using-pytorch/)
- [ ] [Doc: Huggingface Summary of the models](https://huggingface.co/transformers/summary.html)
- [ ] [Doc: Summary of the tokenizers](https://huggingface.co/transformers/master/tokenizer_summary.html)
- [ ] [Article: OpenAI's GPT-3 Language Model: A Technical Overview](https://lambdalabs.com/blog/demystifying-gpt-3/)
- [ ] [Article: Adapting Text Augmentation to Industry problems](https://gitlost-murali.github.io/blogs/nlp/augmentation/exploiting-contextual-models-for-data)
- [ ] [Article: How I Used Deep Learning To Train A Chatbot To Talk Like Me (Sorta)](https://adeshpande3.github.io/adeshpande3.github.io/How-I-Used-Deep-Learning-to-Train-a-Chatbot-to-Talk-Like-Me)
- [ ] [Article: Semantic Entailment](https://trishalaneeraj.github.io/2017-12-22/semantic-entailment)
- [ ] [Article: Feature-based Approach with BERT](https://trishalaneeraj.github.io/2020-04-04/feature-based-approach-with-bert)
- [ ] [Article: Introduction to recurrent neural networks](https://www.jeremyjordan.me/introduction-to-recurrent-neural-networks/)
- [ ] [Article: Aspect-Based Opinion Mining (NLP with Python)](https://medium.com/@pmin91/aspect-based-opinion-mining-nlp-with-python-a53eb4752800)
- [ ] [Article: The Transformer Explained](https://nostalgebraist.tumblr.com/post/185326092369/the-transformer-explained)
- [ ] [Article: Controlling Text Generation with Plug and Play Language Models](https://eng.uber.com/pplm/)
- [ ] [Article: What makes a good conversation?](http://www.abigailsee.com/2019/08/13/what-makes-a-good-conversation.html)
- [ ] [Article: NLP for Supervised Learning - A Brief Survey](https://eugeneyan.com/writing/nlp-supervised-learning-survey/)
- [ ] [Article: Generating Questions Using Transformers](https://amontgomerie.github.io/2020/07/30/question-generator.html)
- [ ] [Article: Neural Language Models as Domain-Specific Knowledge Bases](https://www.statestitle.com/resource/neural-language-models-as-domain-specific-knowledge-bases/)
- [ ] [Article: Understanding BERT’s Semantic Interpretations](https://www.statestitle.com/resource/understanding-berts-semantic-interpretations/)
- [ ] [Article: Using NLP (BERT) to improve OCR accuracy](https://www.statestitle.com/resource/using-nlp-bert-to-improve-ocr-accuracy/)
- [ ] [Article: Hyperparameter Optimization for 🤗Transformers: A guide](https://medium.com/distributed-computing-with-ray/hyperparameter-optimization-for-transformers-a-guide-c4e32c6c989b)
- [ ] [Article: Faster and smaller quantized NLP with Hugging Face and ONNX Runtime](https://medium.com/microsoftazure/faster-and-smaller-quantized-nlp-with-hugging-face-and-onnx-runtime-ec5525473bb7)
- [ ] [Article: Text Similarities : Estimate the degree of similarity between two texts](https://medium.com/@adriensieg/text-similarities-da019229c894)
- [ ] [Article: Reducing Toxicity in Language Models](https://lilianweng.github.io/lil-log/2021/03/21/reducing-toxicity-in-language-models.html)
- [ ] [Article: Learning Word Embedding](https://lilianweng.github.io/lil-log/2017/10/15/learning-word-embedding.html)
- [ ] [Article: The Transformer Family](https://lilianweng.github.io/lil-log/2020/04/07/the-transformer-family.html)
- [ ] [Article: Generalized Language Models](https://lilianweng.github.io/lil-log/2019/01/31/generalized-language-models.html)
- [ ] [Article: Document clustering](https://theaisummer.com/Document_clustering/)
- [ ] [Article: The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
- [ ] [Article: LSTM Primer With Real Life Application( DeepMind Kidney Injury Prediction )*](https://medium.com/@ranko.mosic/lstm-primer-6d7e1cfa704a)
- [ ] [Article: Making sense of LSTMs by example](https://alexander-schiendorfer.github.io/2020/02/08/making-sense-of-lstms.html)
- [ ] [Article: 3 subword algorithms help to improve your NLP model performance](https://medium.com/@makcedward/how-subword-helps-on-your-nlp-model-83dd1b836f46)
- [ ] [Article: Exploring LSTMs](http://blog.echen.me/2017/05/30/exploring-lstms/)
- [ ] [Article: Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
- [ ] [Article: 74 Summaries of Machine Learning and NLP Research](http://www.marekrei.com/blog/74-summaries-of-machine-learning-and-nlp-research/)
- [ ] [Article: Smart Batching Tutorial - Speed Up BERT Training](https://mccormickml.com/2020/07/29/smart-batching-tutorial/)
- [ ] [Article: GPU Benchmarks for Fine-Tuning BERT](https://mccormickml.com/2020/07/21/gpu-benchmarks-for-fine-tuning-bert/)
- [ ] [Article: Existing Tools for Named Entity Recognition](https://mccormickml.com/2020/05/19/existing-ner-tools/)
- [ ] [Article: Domain-Specific BERT Models](https://mccormickml.com/2020/06/22/domain-specific-bert-tutorial/)
- [ ] [Article: Search (Pt 1) — A Gentle Introduction](https://towardsdatascience.com/search-pt-1-a-gentle-introduction-335656c0f814)
- [ ] [Article: Search (Pt 2) — A Semantic Horse Race](https://towardsdatascience.com/search-pt-2-semantic-horse-race-5128cae7ce8d)
- [ ] [Article: Search (Pt 3) — Elastic Transformers](https://towardsdatascience.com/elastic-transformers-ae011e8f5b88)
- [ ] [Article: How to Implement a Beam Search Decoder for Natural Language Processing](https://machinelearningmastery.com/beam-search-decoder-natural-language-processing/)
- [ ] [Article: Speller100: Zero-shot spelling correction at scale for 100-plus languages](https://www.microsoft.com/en-us/research/blog/speller100-zero-shot-spelling-correction-at-scale-for-100-plus-languages/)
- [ ] [Article: Breaking the spell of the spelling check](https://www.slideshare.net/KhrystynaKulak/breaking-the-spell-of-the-spelling-check)
- [ ] [Article: DaCy: New Fast and Efficient State-of-the-Art in Danish NLP!](https://www.kennethenevoldsen.com/post/new-fast-and-efficient-state-of-the-art-in-danish-nlp/)
- [ ] [A friendly introduction to Recurrent Neural Networks](https://www.youtube.com/watch?v=UNmqTiOnRfg)
- [ ] [Book: Embeddings in Natural Language Processing](http://josecamachocollados.com/book_embNLP_draft.pdf)
- [ ] [Book: Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax](https://www.morganclaypool.com/doi/abs/10.2200/S00493ED1V01Y201303HLT020)
- [ ] [Coursera: Sequence Models](https://www.coursera.org/learn/nlp-sequence-models)
- [ ] [Coursera: Natural Language Processing in TensorFlow](https://www.coursera.org/learn/natural-language-processing-tensorflow)
- [ ] [CMU: Low-resource NLP Bootcamp 2020](https://www.youtube.com/playlist?list=PL8PYTP1V4I8A1CpCzURXAUa6H4HO7PF2c)
	- [ ] [CMU Low resource NLP Bootcamp 2020 (1): NLP Tasks](https://www.youtube.com/watch?v=glIbcpay1-I) `1:46:06`
	- [ ] [CMU Low resource NLP Bootcamp 2020 (2): Linguistics - Phonology and Morphology](https://www.youtube.com/watch?v=KGOYGONxypA) `1:24:08`
	- [ ] [CMU Low resource NLP Bootcamp 2020 (3): Machine Translation](https://www.youtube.com/watch?v=SIZfkGzyVRc) `1:55:59`
	- [ ] [CMU Low resource NLP Bootcamp 2020 (4): Linguistics - Syntax and Morphosyntax](https://www.youtube.com/watch?v=j2vd3bTfrIA) `2:00:21`
	- [ ] [CMU Low resource NLP Bootcamp 2020 (5): Neural Representation Learning](https://www.youtube.com/watch?v=FgYg1ZH5Io8) `1:19:57`
	- [ ] [CMU Low resource NLP Bootcamp 2020 (6): Multilingual NLP](https://www.youtube.com/watch?v=wWE4db9XgHA) `2:04:34`
	- [ ] [CMU Low resource NLP Bootcamp 2020 (7): Speech Synthesis](https://www.youtube.com/watch?v=eDjtEsOvouM) `2:22:14`
- [ ] [CMU: Neural Nets for NLP 2020](https://www.youtube.com/playlist?list=PL8PYTP1V4I8CJ7nMxMC8aXv8WqKYwj-aJ)
	- [ ] [CMU Neural Nets for NLP 2020 (1): Introduction](https://www.youtube.com/watch?v=D7o2Z1tAuQc) `1:11:38`
	- [ ] [CMU Neural Nets for NLP 2020 (2): Language Modeling, Efficiency/Training Tricks](https://www.youtube.com/watch?v=aTxfVIzyN4o) `1:18:31`
	- [ ] [CMU Neural Nets for NLP 2020 (3): Convolutional Neural Networks for Text](https://www.youtube.com/watch?v=UirRyzNq3nM) `0:54:45`
	- [ ] [CMU Neural Nets for NLP 2020 (4): Recurrent Neural Networks](https://www.youtube.com/watch?v=wD-mB2clN_0) `1:11:28`
	- [ ] [CMU Neural Nets for NLP 2020 (5): Efficiency Tricks for Neural Nets](https://www.youtube.com/watch?v=eokkF3qv8_U) `1:05:37`
	- [ ] [CMU Neural Nets for NLP 2020 (6): Conditioned Generation](https://www.youtube.com/watch?v=Og3_LngQE_4) `1:07:13`
	- [ ] [CMU Neural Nets for NLP 2020 (7): Attention](https://www.youtube.com/watch?v=jDaJYOmF2iQ) `1:05:26`
	- [ ] [CMU Neural Nets for NLP 2020 (8): Distributional Semantics and Word Vectors](https://www.youtube.com/watch?v=RRaU7pz2eT4) `1:10:45`
	- [ ] [CMU Neural Nets for NLP 2020 (9): Sentence and Contextual Word Representations](https://www.youtube.com/watch?v=EjoTMiZPVC8) `1:16:19`
	- [ ] [CMU Neural Nets for NLP 2020 (10): Debugging Neural Nets (for NLP)](https://www.youtube.com/watch?v=-I-3qRg3ExI) `1:15:26`
	- [ ] [CMU Neural Nets for NLP 2020 (11): Structured Prediction with Local Independence Assumptions](https://www.youtube.com/watch?v=ry-__6gNSqE) `1:08:38`
	- [ ] [CMU Neural Nets for NLP 2020 (12): Generating Trees Incrementally](https://www.youtube.com/watch?v=-bG-QfVrsYw) `1:14:13`
	- [ ] [CMU Neural Nets for NLP 2020 (13): Generating Trees Incrementally](https://www.youtube.com/watch?v=8f_IzoafNgc) `0:51:58`
	- [ ] [CMU Neural Nets for NLP 2020 (14): Search-based Structured Prediction](https://www.youtube.com/watch?v=9OA8IybwI00) `1:06:19`
	- [ ] [CMU Neural Nets for NLP 2020 (15): Minimum Risk Training and Reinforcement Learning](https://www.youtube.com/watch?v=W_x7BL-8VZc) `1:09:16`
	- [ ] [CMU Neural Nets for NLP 2020 (16): Advanced Search Algorithms](https://www.youtube.com/watch?v=mfOCPBOHVjY) `1:03:02`
	- [ ] [CMU Neural Nets for NLP 2020 (17): Adversarial Methods](https://www.youtube.com/watch?v=4SjdBB64mjo) `1:14:55`
	- [ ] [CMU Neural Nets for NLP 2020 (18): Models w/ Latent Random Variables](https://www.youtube.com/watch?v=5OL1_YECHvM) `1:13:16`
	- [ ] [CMU Neural Nets for NLP 2020 (19): Unsupervised and Semi-supervised Learning of Structure](https://www.youtube.com/watch?v=rpAzfgr3OGc) `1:12:47`
	- [ ] [CMU Neural Nets for NLP 2020 (20): Multitask and Multilingual Learning](https://www.youtube.com/watch?v=6_gMeW_cunQ) `1:02:46`
	- [ ] [CMU Neural Nets for NLP 2020 (21): Document Level Models](https://www.youtube.com/watch?v=K72U5dlPwkY) `0:52:04`
	- [ ] [CMU Neural Nets for NLP 2020 (22): Neural Nets + Knowledge Bases](https://www.youtube.com/watch?v=Lcb5YKE21P8) `1:18:39`
	- [ ] [CMU Neural Nets for NLP 2020 (23): Machine Reading w/ Neural Nets](https://www.youtube.com/watch?v=cGHVNwgVLRY) `1:06:11`
	- [ ] [CMU Neural Nets for NLP 2020 (24): Natural Language Generation](https://www.youtube.com/watch?v=dyXTVhDCwCQ) `1:21:48`
	- [ ] [CMU Neural Nets for NLP 2020 (25): Model Interpretation](https://www.youtube.com/watch?v=ePEJqqj7Y8M) `1:04:11`
- [ ] [CMU Multilingual NLP 2020](http://demo.clab.cs.cmu.edu/11737fa20/)
	- [ ] [CMU Multilingual NLP 2020 (1): Introduction](https://www.youtube.com/watch?v=xeu7LKIT194) `1:17:29`
	- [ ] [CMU Multilingual NLP 2020 (2): Typology - The Space of Language](https://www.youtube.com/watch?v=4QilRTLxvCc) `0:37:13`
	- [ ] [CMU Multilingual NLP 2020 (3): Words, Parts of Speech, Morphology](https://www.youtube.com/watch?v=kaSCjn7oMDw) `0:38:58`
	- [ ] [CMU Multilingual NLP 2020 (4): Text Classification and Sequence Labeling](https://www.youtube.com/watch?v=aDvI8-iE0pM) `0:45:56`
	- [ ] [CMU Multilingual NLP 2020 (5): Advanced Text Classification/Labeling](https://www.youtube.com/watch?v=A6gluAlxPMg) `0:49:40`
	- [ ] [CMU Multilingual NLP 2020 (6): Translation, Evaluation, and Datasets](https://www.youtube.com/watch?v=PP5poDIzI8A) `0:46:17`
	- [ ] [CMU Multilingual NLP 2020 (7): Machine Translation/Sequence-to-sequence Models](https://www.youtube.com/watch?v=grVAzbNt4Kk) `0:43:51`
	- [ ] [CMU Multilingual NLP 2020 (8): Data Augmentation for Machine Translation](https://www.youtube.com/watch?v=RqNobMjb6IQ) `0:24:42`
	- [ ] [CMU Multilingual NLP 2020 (9): Language Contact and Similarity Across Languages](https://www.youtube.com/watch?v=brGaYr6bxoY) `0:30:25`
	- [ ] [CMU Multilingual NLP 2020 (10): Multilingual Training and Cross-lingual Transfer](https://www.youtube.com/watch?v=hB4x7a1SDX8) `0:39:58`
	- [ ] [CMU Multilingual NLP 2020 (11): Unsupervised Translation](https://www.youtube.com/watch?v=Z0e3pHZ96No) `0:51:17`
	- [ ] [CMU Multilingual NLP 2020 (12): Code Switching, Pidgins, and Creoles](https://www.youtube.com/watch?v=-jUvZNfkpGQ) `0:46:37`
	- [ ] [CMU Multilingual NLP 2020 (13): Speech](https://www.youtube.com/watch?v=cNaXzF8OKcQ) `0:41:16`
	- [ ] [CMU Multilingual NLP 2020 (14): Automatic Speech Recognition](https://www.youtube.com/watch?v=T2F5fyuk3Jk) `0:39:33`
	- [ ] [CMU Multilingual NLP 2020 (15): Low Resource ASR](https://www.youtube.com/watch?v=wSl61Fuk6R8) `0:43:38`
	- [ ] [CMU Multilingual NLP 2020 (16): Text to Speech](https://www.youtube.com/watch?v=dkGkw-Qxbdw) `0:39:00`
	- [ ] [CMU Multilingual NLP 2020 (17): Morphological Analysis and Inflection](https://www.youtube.com/watch?v=y9sVFrmGu0w) `0:45:22`
	- [ ] [CMU Multilingual NLP 2020 (18): Dependency Parsing](https://www.youtube.com/watch?v=dhlb2F1NyvE) `0:38:15`
	- [ ] [CMU Multilingual NLP 2020 (19): Data Annotation](https://www.youtube.com/watch?v=NP5f0TL_xLU) `0:53:08`
	- [ ] [CMU Multilingual NLP 2020 (20): Active Learning](https://www.youtube.com/watch?v=S4lYp3Si3NI) `0:28:37`
	- [ ] [CMU Multilingual NLP 2020 (21): Information Extraction](https://www.youtube.com/watch?v=uNIy-v2DUgc) `0:41:00`
	- [ ] [CMU Multilingual NLP 2020 (22): Multilingual NLP for Indigenous Languages](https://www.youtube.com/watch?v=wilwuzgLWdk) `1:21:58`
	- [ ] [CMU Multilingual NLP 2020 (23): Universal Translation at Scale](https://www.youtube.com/watch?v=mcl--ktVpjg) `1:27:33`
- [ ] [CMU: MultiModal Machine Learning Fall 2020](https://cmu-multicomp-lab.github.io/mmml-course/fall2020/schedule/)
	- [ ] [Lecture 1.1: Course Introduction](https://www.youtube.com/watch?v=VIq5r7mCAyw)
	- [ ] [Lecture 1.2: Multimodal applications and datasets](https://www.youtube.com/watch?v=fBYu8I52nVM)
	- [ ] [Lecture 2.1: Basic concepts: neural networks](https://www.youtube.com/watch?v=yPrwVpeysG8)
	- [ ] [Lecture 2.2: Basic concepts: network optimization](https://www.youtube.com/watch?v=E_3gxQWaCoQ)
	- [ ] [Lecture 3.1: Visual unimodal representations](https://www.youtube.com/watch?v=skcv-Xo33BM)
	- [ ] [Lecture 3.2: Language unimodal representations](https://www.youtube.com/watch?v=XVHN0izviAw)
	- [ ] [Lecture 4.1: Multimodal representation learning](https://www.youtube.com/watch?v=37z_tJD81y8)
	- [ ] [Lecture 4.2: Coordinated representations](https://www.youtube.com/watch?v=2_dZ5GBlRgU)
	- [ ] [Lecture 5.1: Multimodal alignment](https://www.youtube.com/watch?v=4P4qBBxpthg)
	- [ ] [Lecture 5.2: Alignment and representation](https://www.youtube.com/watch?v=xcOMHwjNLaA)
	- [ ] [Lecture 7.1: Alignment and translation](https://www.youtube.com/watch?v=rLGIrhq8HlQ)
	- [ ] [Lecture 7.2: Probabilistic graphical models](https://www.youtube.com/watch?v=L1TiP9P55-8)
	- [ ] [Lecture 8.1: Discriminative graphical models](https://www.youtube.com/watch?v=ZdR6aljufXk)
	- [ ] [Lecture 8.2: Deep Generative Models](https://www.youtube.com/watch?v=qEbYtPhG768)
	- [ ] [Lecture 9.1: Reinforcement learning](https://www.youtube.com/watch?v=OI02F2XEe_0)
	- [ ] [Lecture 9.2: Multimodal RL](https://www.youtube.com/watch?v=UsAgvMC5fRs)
	- [ ] [Lecture 10.1: Fusion and co-learning](https://www.youtube.com/watch?v=2xr4P0WGKSA)
	- [ ] [Lecture 10.2: New research directions](https://www.youtube.com/watch?v=g3ZpSiwusrM)
- [ ] [Datacamp: Advanced NLP with spaCy](https://www.datacamp.com/courses/advanced-nlp-with-spacy)
- [ ] [Datacamp: Building Chatbots in Python](https://www.datacamp.com/courses/building-chatbots-in-python)
- [ ] [Datacamp: Clustering Methods with SciPy](https://www.datacamp.com/courses/clustering-methods-with-scipy)
- [ ] [Datacamp: Feature Engineering for NLP in Python](https://www.datacamp.com/courses/feature-engineering-for-nlp-in-python)
- [ ] [Datacamp: Machine Translation in Python](https://www.datacamp.com/courses/machine-translation-in-python)
- [ ] [Datacamp: Natural Language Processing Fundamentals in Python](https://www.datacamp.com/courses/natural-language-processing-fundamentals-in-python)
- [ ] [Datacamp: Natural Language Generation in Python](https://www.datacamp.com/courses/natural-language-generation-in-python)
- [ ] [Datacamp: RNN for Language Modeling](https://www.datacamp.com/courses/recurrent-neural-networks-for-language-modeling-in-python)
- [ ] [Datacamp: Regular Expressions in Python](https://www.datacamp.com/courses/regular-expressions-in-python)
- [ ] [Datacamp: Sentiment Analysis in Python](https://www.datacamp.com/courses/sentiment-analysis-in-python)
- [ ] [Datacamp: Spoken Language Processing in Python](https://www.datacamp.com/courses/spoken-language-processing-in-python)
- [ ] [Notebook: NNLM - Predict Next Word](https://github.com/graykode/nlp-tutorial/blob/master/1-1.NNLM)
- [ ] [Notebook: Word2Vec](https://colab.research.google.com/github/graykode/nlp-tutorial/blob/master/1-2.Word2Vec/Word2Vec_Skipgram(Softmax).ipynb)
- [ ] [Notebook: FastText Sentence Classification](https://colab.research.google.com/github/graykode/nlp-tutorial/blob/master/1-3.FastText/FastText.ipynb)
- [ ] [Notebook: TextCNN - Binary Sentiment Classification](https://colab.research.google.com/github/graykode/nlp-tutorial/blob/master/2-1.TextCNN/TextCNN.ipynb)
- [ ] [Notebook: TextRNN - Predict Next Step](https://colab.research.google.com/github/graykode/nlp-tutorial/blob/master/3-1.TextRNN/TextRNN.ipynb)
- [ ] [Notebook: TextLSTM - Autocomplete](https://colab.research.google.com/github/graykode/nlp-tutorial/blob/master/3-2.TextLSTM/TextLSTM.ipynb)
- [ ] [Notebook: Bi-LSTM - Predict Next Word in Long Sentence](https://colab.research.google.com/github/graykode/nlp-tutorial/blob/master/3-3.Bi-LSTM/Bi_LSTM.ipynb)
- [ ] [Notebook: SeqSeq - Change Word](https://colab.research.google.com/github/graykode/nlp-tutorial/blob/master/4-1.Seq2Seq/Seq2Seq.ipynb)
- [ ] [Notebook: Seq2Seq with Attention - Translate](https://colab.research.google.com/github/graykode/nlp-tutorial/blob/master/4-2.Seq2Seq(Attention)/Seq2Seq(Attention).ipynb)
- [ ] [Notebook: Bi-LSTM with Attention - Binary Sentiment Classification](https://colab.research.google.com/github/graykode/nlp-tutorial/blob/master/4-3.Bi-LSTM(Attention)/Bi_LSTM(Attention).ipynb)
- [ ] [Notebook: The Transformer - Translate](https://colab.research.google.com/github/graykode/nlp-tutorial/blob/master/5-1.Transformer/Transformer.ipynb)
- [ ] [Notebook: The Transformer - Greedy Decoder](https://colab.research.google.com/github/graykode/nlp-tutorial/blob/master/5-1.Transformer/Transformer(Greedy_decoder).ipynb)
- [ ] [Notebook: BERT - NSP and MLM](https://colab.research.google.com/github/graykode/nlp-tutorial/blob/master/5-2.BERT/BERT.ipynb)
- [ ] [RNN and LSTM](https://www.youtube.com/watch?v=WCUNPb-5EYI&index=2&list=PLVZqlMpoM6kbaeySxhdtgQPFEC5nV7Faa&t=0s)
- [ ] [Spacy Tutorial](https://www.youtube.com/watch?v=cgwDB1THUBY&list=PLJ39kWiJXSiz1LK8d_fyxb7FTn4mBYOsD)
- [ ] [Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/playlist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20)
	- [ ] [Lecture 1  – Course Overview | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=tZ_Jrc_nRJY) `1:12:59`
	- [ ] [Lecture 2  – Word Vectors 1 | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=IYMYI9AJpQs) `1:17:10`
	- [ ] [Lecture 3  – Word Vectors 2 | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=nH4rn3X8i0c) `1:16:52`
	- [ ] [Lecture 4  – Word Vectors 3 | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=pip8h9vjTHY) `0:38:20`
	- [ ] [Lecture 5 – Sentiment Analysis 1 | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=O1Xh3H1uEYY) `1:10:44`
	- [ ] [Lecture 6 – Sentiment Analysis 2 | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=6-4pJt1M18s) `1:03:23`
	- [ ] [Lecture 7 – Relation Extraction | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=pO3Jsr31s_Q) `1:19:04`
	- [ ] [Lecture 8 – NLI 1 | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=M_VPUF9ResU) `1:15:02`
	- [ ] [Lecture 9 – NLI 2 | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=JXtH_ABQFX0) `1:15:35`
	- [ ] [Lecture 10 – Grounding | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=7b2_3dDTKMc) `1:23:15`
	- [ ] [Lecture 11 – Semantic Parsing | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=C5bdflsg7rs) `1:07:05`
	- [ ] [Lecture 12 – Evaluation Methods | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=3UGti9Ju5j8) `1:18:32`
	- [ ] [Lecture 13 – Evaluation Metrics | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=YygGzfkhtJc) `1:11:32`
	- [ ] [Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=lzBB7xoZ3Q8) `1:14:33`
	- [ ] [Lecture 15 – Presenting Your Work | Stanford CS224U: Natural Language Understanding | Spring 2019](https://www.youtube.com/watch?v=WXLb4h2A724) `1:15:11`
- [] [Stanford CS224N: Stanford CS224N: NLP with Deep Learning | Winter 2019](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z)
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectors](https://www.youtube.com/watch?v=8rXD5-xhemo) `1:21:52`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 2 – Word Vectors and Word Senses](https://www.youtube.com/watch?v=kEMJRjEdNzM) `1:20:43`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 3 – Neural Networks](https://www.youtube.com/watch?v=8CWyBNX6eDo) `1:18:50`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 4 – Backpropagation](https://www.youtube.com/watch?v=yLYHDSv-288) `1:22:15`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 5 – Dependency Parsing](https://www.youtube.com/watch?v=nC9_RfjYwqA) `1:20:22`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 6 – Language Models and RNNs](https://www.youtube.com/watch?v=iWea12EAu6U) `1:08:25`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 7 – Vanishing Gradients, Fancy RNNs](https://www.youtube.com/watch?v=QEw0qEa0E50) `1:13:23`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 – Translation, Seq2Seq, Attention](https://www.youtube.com/watch?v=XXtpJxZBa2c) `1:16:56`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 9 – Practical Tips for Projects](https://www.youtube.com/watch?v=fyqm8fRDgl0) `1:22:39`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 10 – Question Answering](https://www.youtube.com/watch?v=yIdF-17HwSk) `1:21:01`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 11 – Convolutional Networks for NLP](https://www.youtube.com/watch?v=EAJoRA0KX7I) `1:20:18`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 12 – Subword Models](https://www.youtube.com/watch?v=9oTHFx0Gg3Q) `1:15:30`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 13 – Contextual Word Embeddings](https://www.youtube.com/watch?v=S-CspeZ8FHc) `1:20:18`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 14 – Transformers and Self-Attention](https://www.youtube.com/watch?v=5vcj8kSwBCY) `0:53:48`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 15 – Natural Language Generation](https://www.youtube.com/watch?v=4uG1NMKNWCU) `1:19:37`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 16 – Coreference Resolution](https://www.youtube.com/watch?v=i19m4GzBhfc) `1:19:20`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 17 – Multitask Learning](https://www.youtube.com/watch?v=M8dsZsEtEsg) `1:11:54`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 18 – Constituency Parsing, TreeRNNs](https://www.youtube.com/watch?v=6Z4A3RSf-HY) `1:20:37`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 19 – Bias in AI](https://www.youtube.com/watch?v=XR8YSRcuVLE) `0:56:03`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 20 – Future of NLP + Deep Learning](https://www.youtube.com/watch?v=3wWZBGN-iX8) `1:19:15`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2020 | Low Resource Machine Translation](https://youtu.be/mp95Z5yM92c?list=LL) `1:15:45`
	- [ ] [Stanford CS224N: NLP with Deep Learning | Winter 2020 | BERT and Other Pre-trained Language Models](https://youtu.be/knTc-NQSjKA?list=LL) `0:54:28`
- [ ] [Stanford: Natural Language Processing | Dan Jurafsky, Christopher Manning](https://www.youtube.com/playlist?list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm)
    - [ ] [Course Introduction](https://www.youtube.com/watch?v=3Dt_yh1mf_U) `0:12:52`
    - [ ] [Regular Expressions](https://www.youtube.com/watch?v=zfH2ADGtzJQ) `0:11:25`
    - [ ] [Regular Expressions in Practical NLP](https://www.youtube.com/watch?v=k242_PpMEsQ) `0:06:05`
    - [ ] [Word Tokenization](https://www.youtube.com/watch?v=f9o514a-kuc) `0:14:26`
    - [ ] [Word Normalization and Stemming](https://www.youtube.com/watch?v=ZhyLgPnOeh0) `0:11:48`
    - [ ] [Sentence Segmentation](https://www.youtube.com/watch?v=UL4Ez56AMVo) `0:05:34`
    - [ ] [Defining Minimum Edit Distance](https://www.youtube.com/watch?v=-XRJL2R0SCg) `0:07:05`
    - [ ] [Computing Minimum Edit Distance](https://www.youtube.com/watch?v=fN3Js7H72HE) `0:05:55`
    - [ ] [Backtrace for Computing Alignments](https://www.youtube.com/watch?v=MnE10oJ2jX4) `0:05:56`
    - [ ] [Weighted Minimum Edit Distance](https://www.youtube.com/watch?v=9G-EwniAaPA) `0:02:48`
    - [ ] [Minimum Edit Distance in Computational Biology](https://www.youtube.com/watch?v=96pL6xTP3W8) `0:09:30`
    - [ ] [Introduction to N grams](https://www.youtube.com/watch?v=Saq1QagC8KY) `0:08:41`
    - [ ] [Estimating N gram Probabilities](https://www.youtube.com/watch?v=paCMAZ-lKq8) `0:09:38`
    - [ ] [Evaluation and Perplexity](https://www.youtube.com/watch?v=b6nwdc_fGfA) `0:11:09`
    - [ ] [Generalization and Zeros](https://www.youtube.com/watch?v=6NeUDr7YDiw) `0:05:15`
    - [ ] [Smoothing Add One](https://www.youtube.com/watch?v=ZbHFLgBWgdQ) `0:06:31`
    - [ ] [Interpolation](https://www.youtube.com/watch?v=naNezonMA7k) `0:10:25`
    - [ ] [Good Turing Smoothing](https://www.youtube.com/watch?v=fhvDgKBZa1U) `0:15:35`
    - [ ] [Kneser Ney Smoothing](https://www.youtube.com/watch?v=eNLUo3AIvcQ) `0:08:59`
    - [ ] [The Spelling Correction Task](https://www.youtube.com/watch?v=iYFqYL9RsRs) `0:05:40`
    - [ ] [The Noisy Channel Model of Spelling](https://www.youtube.com/watch?v=q6BFerSaUEA) `0:19:31`
    - [ ] [Real Word Spelling Correction](https://www.youtube.com/watch?v=bZNU2FwTXDc) `0:09:20`
    - [ ] [State of the Art Systems](https://www.youtube.com/watch?v=aAvOOZRsby8) `0:07:10`
    - [ ] [What is Text Classification](https://www.youtube.com/watch?v=kxImnFg4ZiQ) `0:08:12`
    - [ ] [Naive Bayes](https://www.youtube.com/watch?v=j39c7Gjx2gE) `0:03:20`
    - [ ] [Formalizing the Naive Bayes Classifier](https://www.youtube.com/watch?v=VNEdufXVMaU) `0:09:29`
    - [ ] [Naive Bayes Learning](https://www.youtube.com/watch?v=3jR8TZG8T88) `0:05:23`
    - [ ] [Naive Bayes Relationship to Language Modeling](https://www.youtube.com/watch?v=LRFdF9J__Tc) `0:04:36`
    - [ ] [Multinomial Naive Bayes A Worked Example](https://www.youtube.com/watch?v=OWGVQfuvNMk) `0:08:59`
    - [ ] [Precision, Recall, and the F measure](https://www.youtube.com/watch?v=81j2nzzBHUw) `0:16:17`
    - [ ] [Text Classification Evaluation](https://www.youtube.com/watch?v=TdkWIxGoiak) `0:07:17`
    - [ ] [Practical Issues in Text Classification](https://www.youtube.com/watch?v=uIvSHmsLs-U) `0:05:57`
    - [ ] [What is Sentiment Analysis](https://www.youtube.com/watch?v=vy0HC5H-484) `0:07:18`
    - [ ] [Sentiment Analysis A baseline algorithm](https://www.youtube.com/watch?v=Dgqt62RQMaY) `0:13:27`
    - [ ] [Sentiment Lexicons](https://www.youtube.com/watch?v=wBE0FE_2ddE) `0:08:38`
    - [ ] [Learning Sentiment Lexicons](https://www.youtube.com/watch?v=Z7RxBcpyN1U) `0:14:46`
    - [ ] [Other Sentiment Tasks](https://www.youtube.com/watch?v=3Eo--0_ocIk) `0:11:02`
    - [ ] [Generative vs  Discriminative Models](https://www.youtube.com/watch?v=YQClUDd9ff4) `0:07:50`
    - [ ] [Making features from text for discriminative NLP models](https://www.youtube.com/watch?v=MemiaOYSB0k) `0:18:12`
    - [ ] [Feature Based Linear Classifiers](https://www.youtube.com/watch?v=7-7MlBdy3EE) `0:13:35`
    - [ ] [Building a Maxent Model  The Nuts and Bolts](https://www.youtube.com/watch?v=uRJjelMVcPA) `0:08:05`
    - [ ] [Generative vs Discriminative models](https://www.youtube.com/watch?v=_Niy2f-M9KA) `0:12:10`
    - [ ] [Maximizing the Likelihood](https://www.youtube.com/watch?v=E1k2bG6fKrY) `0:10:30`
    - [ ] [Introduction to Information Extraction](https://www.youtube.com/watch?v=ARsDDLffoMk) `0:09:19`
    - [ ] [Evaluation of Named Entity Recognition](https://www.youtube.com/watch?v=0qWDkRdWbSw) `0:06:35`
    - [ ] [Sequence Models for Named Entity Recognition](https://www.youtube.com/watch?v=m8RrR5GORLg) `0:15:06`
    - [ ] [Maximum Entropy Sequence Models](https://www.youtube.com/watch?v=s2QL2VML0Qo) `0:13:02`
    - [ ] [What is Relation Extraction](https://www.youtube.com/watch?v=gTFMULX7vU0) `0:09:47`
    - [ ] [Using Patterns to Extract Relations](https://www.youtube.com/watch?v=kSxfsvF9KQE) `0:06:17`
    - [ ] [Supervised Relation Extraction](https://www.youtube.com/watch?v=a48vIACEC_w) `0:10:51`
    - [ ] [Semi Supervised and Unsupervised Relation Extraction](https://www.youtube.com/watch?v=Jxqx70iX9kc) `0:09:53`
    - [ ] [The Maximum Entropy Model Presentation](https://www.youtube.com/watch?v=GGBz6FySsdA) `0:12:14`
    - [ ] [Feature Overlap Feature Interaction](https://www.youtube.com/watch?v=4pKTLEIJhx0) `0:12:52`
    - [ ] [Conditional Maxent Models for Classification](https://www.youtube.com/watch?v=xn6zU9ds-oA) `0:04:11`
    - [ ] [Smoothing Regularization Priors for Maxent Models](https://www.youtube.com/watch?v=LRNI90r9MHk) `0:29:24`
    - [ ] [An Intro to Parts of Speech and POS Tagging](https://www.youtube.com/watch?v=JhJU0Akkqzo) `0:13:19`
    - [ ] [Some Methods and Results on Sequence Models for POS Tagging](https://www.youtube.com/watch?v=Zm_bmRhbaQg) `0:13:04`
    - [ ] [Syntactic Structure Constituency vs Dependency](https://www.youtube.com/watch?v=XCm4js8wp4A) `0:08:46`
    - [ ] [Empirical Data Driven Approach to Parsing](https://www.youtube.com/watch?v=xLa2A4pBF6I) `0:07:11`
    - [ ] [The Exponential Problem in Parsing](https://www.youtube.com/watch?v=zJ_wC4EW_ak) `0:14:31`
    - [ ] [Instructor Chat](https://www.youtube.com/watch?v=zw5nhoabB_U) `0:09:03`
    - [ ] [CFGs and PCFGs](https://www.youtube.com/watch?v=Z6GsoBA-09k) `0:15:30`
    - [ ] [Grammar Transforms](https://www.youtube.com/watch?v=3WzZL-2zAz0) `0:12:06`
    - [ ] [CKY Parsing](https://www.youtube.com/watch?v=CFEGKVjEH1Q) `0:23:26`
    - [ ] [CKY Example](https://www.youtube.com/watch?v=MIlQwfMZ8O4) `0:21:25`
    - [ ] [Constituency Parser Evaluation](https://www.youtube.com/watch?v=_JtP-32keKE) `0:09:46`
    - [ ] [Lexicalization of PCFGs](https://www.youtube.com/watch?v=FKLfzBgVTeA) `0:07:03`
    - [ ] [Charniak's Model](https://www.youtube.com/watch?v=3NowJlNeXNg) `0:18:24`
    - [ ] [PCFG Independence Assumptions](https://www.youtube.com/watch?v=xWXEcIaK30U) `0:09:44`
    - [ ] [The Return of Unlexicalized PCFGs](https://www.youtube.com/watch?v=VqDAUymCFEY) `0:20:53`
    - [ ] [Latent Variable PCFGs](https://www.youtube.com/watch?v=XFV_lkyt_QI) `0:12:08`
    - [ ] [Dependency Parsing Introduction](https://www.youtube.com/watch?v=1_LQscB4Wso) `0:10:25`
    - [ ] [Greedy Transition Based Parsing](https://www.youtube.com/watch?v=egBq3gi_4No) `0:31:05`
    - [ ] [Dependencies Encode Relational Structure](https://www.youtube.com/watch?v=Ec5g2Y23uTw) `0:07:21`
    - [ ] [Introduction to Information Retrieval](https://www.youtube.com/watch?v=9GaPPnCtbT8) `0:09:16`
    - [ ] [Term Document Incidence Matrices](https://www.youtube.com/watch?v=9Ib0SQkQmNA) `0:08:59`
    - [ ] [The Inverted Index](https://www.youtube.com/watch?v=c0WqESk50nc) `0:10:43`
    - [ ] [Query Processing with the Inverted Index](https://www.youtube.com/watch?v=JqMJKNkxy-w) `0:06:44`
    - [ ] [Phrase Queries and Positional Indexes](https://www.youtube.com/watch?v=e2_Nwaffr3s) `0:19:46`
    - [ ] [Introducing Ranked Retrieval](https://www.youtube.com/watch?v=Ruq1pVFdvz8) `0:04:27`
    - [ ] [Scoring with the Jaccard Coefficient](https://www.youtube.com/watch?v=j_vuazcHjN8) `0:05:07`
    - [ ] [Term Frequency Weighting](https://www.youtube.com/watch?v=iLT6ddK_paI) `0:06:00`
    - [ ] [Inverse Document Frequency Weighting](https://www.youtube.com/watch?v=2MuPP7tFgMQ) `0:10:17`
    - [ ] [TF IDF Weighting](https://www.youtube.com/watch?v=hNXwhF0OZ_o) `0:03:42`
    - [ ] [The Vector Space Model](https://www.youtube.com/watch?v=QV0LrPpCA9Y) `0:16:23`
    - [ ] [Calculating TF IDF Cosine Scores](https://www.youtube.com/watch?v=SzwP2QKBZhY) `0:12:48`
    - [ ] [Evaluating Search Engines](https://www.youtube.com/watch?v=KWc6Dkp33uQ) `0:09:03`
    - [ ] [Word Senses and Word Relations](https://www.youtube.com/watch?v=QRhuv7_t0Gc) `0:11:50`
    - [ ] [WordNet and Other Online Thesauri](https://www.youtube.com/watch?v=YJBYXwP0AkE) `0:06:23`
    - [ ] [Word Similarity and Thesaurus Methods](https://www.youtube.com/watch?v=KHxbsDg4w6M) `0:16:18`
    - [ ] [Word Similarity Distributional Similarity I](https://www.youtube.com/watch?v=ourfgzdoG1g) `0:13:15`
    - [ ] [Word Similarity Distributional Similarity II](https://www.youtube.com/watch?v=Mn0JvsSFzs8) `0:08:16`
    - [ ] [What is Question Answering](https://www.youtube.com/watch?v=U8AvpqVdgBw) `0:07:29`
    - [ ] [Answer Types and Query Formulation](https://www.youtube.com/watch?v=MB7Ec3XTNkE) `0:08:48`
    - [ ] [Passage Retrieval and Answer Extraction](https://www.youtube.com/watch?v=corkeW31Pr0) `0:06:38`
    - [ ] [Using Knowledge in QA](https://www.youtube.com/watch?v=Oy2yprz3bUc) `0:04:25`
    - [ ] [Advanced Answering Complex Questions](https://www.youtube.com/watch?v=g24fe_6HNuo) `0:04:53`
    - [ ] [Introduction to Summarization](https://www.youtube.com/watch?v=wUF9r2pMEag) `0:04:46`
    - [ ] [Generating Snippets](https://www.youtube.com/watch?v=3JvlZmUaM-A) `0:07:35`
    - [ ] [Evaluating Summaries ROUGE](https://www.youtube.com/watch?v=LoXmdN_WpFw) `0:05:03`
    - [ ] [Summarizing Multiple Documents](https://www.youtube.com/watch?v=tKxVJcalVr4) `0:10:42`
    - [ ] [Instructor Chat II](https://www.youtube.com/watch?v=l65_uHR4bww) `0:05:24`
- [ ] [TextBlob Tutorial Series](https://www.youtube.com/watch?v=4k2cqUIjb8g&list=PLJ39kWiJXSizrWpC7hcu1_mLNxEPzN0gF)
	- [ ] [Natural Language Processing Tutorial With TextBlob -Tokens,Translation and Ngrams](https://www.youtube.com/watch?v=4k2cqUIjb8g) `0:11:01`
	- [ ] [NLP Tutorial With TextBlob and Python - Parts of Speech Tagging](https://www.youtube.com/watch?v=aWhqoPLr6Jg) `0:05:59`
	- [ ] [NLP Tutorial With TextBlob & Python - Lemmatizating](https://www.youtube.com/watch?v=tNUoqSlzM_k) `0:06:32`
	- [ ] [NLP Tutorial  with TextBlob &  Python - Sentiment Analysis(Polarity,Subjectivity)](https://www.youtube.com/watch?v=dR9Dcq-qgIE) `0:06:31`
	- [ ] [Building a NLP-based Flask App with TextBlob](https://www.youtube.com/watch?v=7tLBHkqMae8) `0:37:30`
	- [ ] [Natural Language Processing with Polyglot - Installation & Intro](https://www.youtube.com/watch?v=qtMEp6WxwCQ) `0:12:49`
- [ ] [Youtube: fast.ai Code-First Intro to Natural Language Processing](https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9)
	- [ ] [What is NLP? (NLP video 1)](https://www.youtube.com/watch?v=cce8ntxP_XI) `0:22:42`
	- [ ] [Topic Modeling with SVD & NMF (NLP video 2)](https://www.youtube.com/watch?v=tG3pUwmGjsc) `1:06:39`
	- [ ] [Topic Modeling & SVD revisited (NLP video 3)](https://www.youtube.com/watch?v=lRZ4aMaXPBI) `0:33:05`
	- [ ] [Sentiment Classification with Naive Bayes (NLP video 4)](https://www.youtube.com/watch?v=hp2ipC5pW4I) `0:58:20`
	- [ ] [Sentiment Classification with Naive Bayes & Logistic Regression, contd. (NLP video 5)](https://www.youtube.com/watch?v=dt7sArnLo1g) `0:51:29`
	- [ ] [Derivation of Naive Bayes & Numerical Stability (NLP video 6)](https://www.youtube.com/watch?v=z8-Tbrg1-rE) `0:23:56`
	- [ ] [Revisiting Naive Bayes, and Regex (NLP video 7)](https://www.youtube.com/watch?v=Q1zLqfnEXdw) `0:37:33`
	- [ ] [Intro to Language Modeling (NLP video 8)](https://www.youtube.com/watch?v=PNNHaQUQqW8) `0:40:58`
	- [ ] [Transfer learning (NLP video 9)](https://www.youtube.com/watch?v=5gCQvuznKn0) `1:35:16`
	- [ ] [ULMFit for non-English Languages (NLP Video 10)](https://www.youtube.com/watch?v=MDX_x6rKXAs) `1:49:22`
	- [ ] [Understanding RNNs (NLP video 11)](https://www.youtube.com/watch?v=l1rlFh0PmZw) `0:33:16`
	- [ ] [Seq2Seq Translation (NLP video 12)](https://www.youtube.com/watch?v=IfsjMg4fLWQ) `0:59:42`
	- [ ] [Word embeddings quantify 100 years of gender & ethnic stereotypes-- Nikhil Garg (NLP video 13)](https://www.youtube.com/watch?v=boxV8Od4jqQ) `0:47:17`
	- [ ] [Text generation algorithms (NLP video 14)](https://www.youtube.com/watch?v=3oEb_fFmPnY) `0:25:39`
	- [ ] [Implementing a GRU (NLP video 15)](https://www.youtube.com/watch?v=Bl6WVj6wQaE) `0:23:13`
	- [ ] [Algorithmic Bias (NLP video 16)](https://www.youtube.com/watch?v=pThqge9QDn8) `1:26:17`
	- [ ] [Introduction to the Transformer (NLP video 17)](https://www.youtube.com/watch?v=AFkGPmU16QA) `0:22:54`
	- [ ] [The Transformer for language translation (NLP video 18)](https://www.youtube.com/watch?v=KzfyftiH7R8) `0:55:17`
	- [ ] [What you need to know about Disinformation (NLP video 19)](https://www.youtube.com/watch?v=vbva2RN-rbQ) `0:51:21`
    - [ ] [Article: Zero to Hero with fastai - Beginner](https://muellerzr.github.io/fastblog/2020/08/20/_08_21-beginner.html)
    - [ ] [Article: Zero to Hero with fastai - Intermediate](https://muellerzr.github.io/fastblog/2020/08/20/_08_21-intermediate.html)
- [ ] [NLP Course | For You](https://lena-voita.github.io/nlp_course.html)
    - [ ] [Word Embeddings](https://lena-voita.github.io/nlp_course/word_embeddings.html)
    - [ ] [Text Classification](#)
    - [ ] [Language Modeling](#)
    - [ ] [Seq2seq and Attention](#)
- [ ] [Youtube: BERT Research Series](https://www.youtube.com/playlist?list=PLam9sigHPGwOBuH4_4fr-XvDbe5uneaf6)
- [ ] [YouTube: Intro to NLP with Spacy](https://www.youtube.com/playlist?list=PLBmcuObd5An559HbDr_alBnwVsGq-7uTF)
- [ ] [Talk: Practical NLP for the Real World](https://www.infoq.com/presentations/practical-nlp/)
- [ ] [YouTube: Level 3 AI Assistant Conference 2020](https://www.youtube.com/playlist?list=PL75e0qA87dlGP51yZ0dyNup-vwu0Rlv86)
  - [ ] [Youtube: Conversation Analysis Theory in Chatbots | Michael Szul](https://youtu.be/osQChzuhUiU?list=PL75e0qA87dlGP51yZ0dyNup-vwu0Rlv86)
  - [ ] [Youtube: Designing Practical NLP Solutions | Ines Montani](https://youtu.be/JpkzK58lkmA?list=PL75e0qA87dlGP51yZ0dyNup-vwu0Rlv86)
  - [ ] [Youtube: Effective Copywriting for Chatbots | Hans Van Dam](https://youtu.be/49G58PQWO7w?list=LLqn7Nv8Zg6tWbBonrUOJGwQ)
  - [ ] [Youtube: Distilling BERT | Sam Sucik](https://youtu.be/Xji8NmL3FvQ?list=LLqn7Nv8Zg6tWbBonrUOJGwQ)
  - [ ] [Youtube: Transformer Policies that improve Dialogues: A Live Demo by Vincent Warmerdam](https://youtu.be/P5SUS3V50zQ?list=LLqn7Nv8Zg6tWbBonrUOJGwQ)
  - [ ] [Youtube: From Research to Production – Our Process at Rasa | Tanja Bunk](https://youtu.be/5_lRfLFjfEs?list=LLqn7Nv8Zg6tWbBonrUOJGwQ)
  - [ ] [Youtube: Keynote: Perspective on the 5 Levels of Conversational AI | Alan Nichol](https://youtu.be/bAkToyQhWyo?list=LLqn7Nv8Zg6tWbBonrUOJGwQ)
- [ ] [Youtube: RASA Algorithm Whiteboard](https://www.youtube.com/playlist?list=PL75e0qA87dlG-za8eLI6t0_Pbxafk-cxb)
	- [ ] [Introducing The Algorithm Whiteboard](https://www.youtube.com/watch?v=wWNMST6t1TA) `0:01:16`
	- [ ] [Rasa Algorithm Whiteboard - Diet Architecture 1: How it Works](https://www.youtube.com/watch?v=vWStcJDuOUk) `0:23:27`
	- [ ] [Rasa Algorithm Whiteboard - Diet Architecture 2: Design Decisions](https://www.youtube.com/watch?v=KUGGuJ0aTL8) `0:15:06`
	- [ ] [Rasa Algorithm Whiteboard - Diet Architecture 3: Benchmarking](https://www.youtube.com/watch?v=oj5oPGDlep4) `0:22:34`
	- [ ] [Rasa Algorithm Whiteboard - Embeddings 1: Just Letters](https://www.youtube.com/watch?v=mWvnlVw_LiY) `0:13:48`
	- [ ] [Rasa Algorithm Whiteboard - Embeddings 2: CBOW and Skip Gram](https://www.youtube.com/watch?v=BWaHLmG1lak) `0:19:24`
	- [ ] [Rasa Algorithm Whiteboard - Embeddings 3: GloVe](https://www.youtube.com/watch?v=QoUYlxl1RGI) `0:19:12`
	- [ ] [Rasa Algorithm Whiteboard - Embeddings 4: Whatlies](https://www.youtube.com/watch?v=FwkwC7IJWO0) `0:14:03`
	- [ ] [Rasa Algorithm Whiteboard - Attention 1: Self Attention](https://www.youtube.com/watch?v=yGTUuEx3GkA) `0:14:32`
	- [ ] [Rasa Algorithm Whiteboard - Attention 2: Keys, Values, Queries](https://www.youtube.com/watch?v=tIvKXrEDMhk) `0:12:26`
	- [ ] [Rasa Algorithm Whiteboard - Attention 3: Multi Head Attention](https://www.youtube.com/watch?v=23XUv0T9L5c) `0:10:55`
	- [ ] [Rasa Algorithm Whiteboard: Attention 4 - Transformers](https://www.youtube.com/watch?v=EXNBy8G43MM) `0:14:34`
	- [ ] [Rasa Algorithm Whiteboard - StarSpace](https://www.youtube.com/watch?v=ZT3_9Kjx7oI) `0:11:46`
	- [ ] [Rasa Algorithm Whiteboard - TED Policy](https://www.youtube.com/watch?v=j90NvurJI4I) `0:16:10`
	- [ ] [Rasa Algorithm Whiteboard - TED in Practice](https://www.youtube.com/watch?v=d8JMJMvErSg) `0:14:54`
	- [ ] [Rasa Algorithm Whiteboard - Response Selection](https://www.youtube.com/watch?v=2jvyWngHEJM) `0:12:07`
	- [ ] [Rasa Algorithm Whiteboard - Response Selection: Implementation](https://www.youtube.com/watch?v=0tXkFScW0hE) `0:09:25`
	- [ ] [Rasa Algorithm Whiteboard - Countvectors](https://www.youtube.com/watch?v=Ju7l5ADg10U) `0:13:32`
	- [ ] [Rasa Algorithm Whiteboard - Subword Embeddings](https://www.youtube.com/watch?v=kNw9dpzp5RU) `0:11:58`
	- [ ] [Rasa Algorithm Whiteboard - Implementation of Subword Embeddings](https://www.youtube.com/watch?v=8D3Gamk1Jig) `0:10:01`
	- [ ] [Rasa Algorithm Whiteboard - BytePair Embeddings](https://www.youtube.com/watch?v=-0IjF-7OB3s) `0:12:44`
- [ ] [Youtube: A brief history of the Transformer architecture in NLP](https://www.youtube.com/watch?v=iH-wmtxHunk)
- [ ] [Youtube: The Transformer neural network architecture explained. “Attention is all you need” (NLP)](https://www.youtube.com/watch?v=FWFA4DGuzSc)
- [ ] [Youtube: How does a Transformer architecture combine Vision and Language? ViLBERT - NLP meets Computer Vision](https://www.youtube.com/watch?v=dd7nE4nbxN0)
- [ ] [Youtube: Strategies for pre-training the BERT-based Transformer architecture – language (and vision)](https://www.youtube.com/watch?v=dabFOBE4eZI)
- [ ] [Youtube: Ilya Sutskever - GPT-2](https://youtu.be/T0I88NhR_9M)
- [ ] [Youtube: NLP Masterclass | Modeling Fallacies in NLP](https://youtu.be/f2m6Mon0VE8?t=223)
- [ ] [Youtube: What is GPT-3? Showcase, possibilities, and implications](https://youtu.be/5fqxPOaaqi0)
- [ ] [Youtube: TextAttack: A Framework for Data Augmentation and Adversarial Training in NLP](https://youtu.be/VpLAjOQHaLU?list=LLqn7Nv8Zg6tWbBonrUOJGwQ)
- [ ] [Article: How the Embedding Layers in BERT Were Implemented](https://medium.com%2F@medium.com/@_init_/why-bert-has-3-embedding-layers-and-their-implementation-details-9c261108e28a)
- [ ] [Youtube: Easy Data Augmentation for Text Classification](https://www.youtube.com/watch?v=3w92peJtYNQ&feature=youtu.be)
- [ ] [Youtube: Webinar: Special NLP Session with Hugging Face](https://www.youtube.com/watch?v=SUqi_E_Lyjs)
- [ ] [Youtube: Spacy IRL 2019](https://www.youtube.com/playlist?list=PLBmcuObd5An4UC6jvK_-eSl6jCvP1gwXc)
	- [ ] [Sebastian Ruder: Transfer Learning in Open-Source Natural Language Processing (spaCy IRL 2019)](https://www.youtube.com/watch?v=hNPwRPg9BrQ) `0:31:24`
	- [ ] [Giannis Daras: Improving sparse transformer models for efficient self-attention (spaCy IRL 2019)](https://www.youtube.com/watch?v=KwKr_e7xBQ4) `0:20:13`
	- [ ] [Peter Baumgartner: Applied NLP: Lessons from the Field (spaCy IRL 2019)](https://www.youtube.com/watch?v=QRGMJWwOU94) `0:18:44`
	- [ ] [Justina Petraitytė: Lessons learned in helping ship conversational AI assistants (spaCy IRL 2019)](https://www.youtube.com/watch?v=1jI0mTcNRUU) `0:23:48`
	- [ ] [Yoav Goldberg: The missing elements in NLP (spaCy IRL 2019)](https://www.youtube.com/watch?v=e12danHhlic) `0:30:27`
	- [ ] [Sofie Van Landeghem: Entity linking functionality in spaCy (spaCy IRL 2019)](https://www.youtube.com/watch?v=PW3RJM8tDGo) `0:20:08`
	- [ ] [Guadalupe Romero: Rethinking rule-based lemmatization (spaCy IRL 2019)](https://www.youtube.com/watch?v=88zcQODyuko) `0:14:49`
	- [ ] [Mark Neumann: ScispaCy: A spaCy pipeline & models for scientific & biomedical text (spaCy IRL 2019)](https://www.youtube.com/watch?v=2_HSKDALwuw) `0:18:59`
	- [ ] [Patrick Harrison: Financial NLP at S&P Global (spaCy IRL 2019)](https://www.youtube.com/watch?v=rdmaR4WRYEM) `0:21:42`
	- [ ] [McKenzie Marshall: NLP in Asset Management (spaCy IRL 2019)](https://www.youtube.com/watch?v=kX14Ycieju8) `0:20:32`
	- [ ] [David Dodson: spaCy in the News: Quartz's NLP pipeline (spaCy IRL 2019)](https://www.youtube.com/watch?v=azrVX8JksMU) `0:20:56`
	- [ ] [Matthew Honnibal & Ines Montani: spaCy and Explosion: past, present & future (spaCy IRL 2019)](https://www.youtube.com/watch?v=Jk9y17lvltY) `0:54:13`
- [ ] [Youtube: The Future of Natural Language Processing](https://youtu.be/G5lmya6eKtc)
- [ ] [Youtube: Sentiment Analysis: Key Milestones, Challenges and New Directions](https://www.youtube.com/watch?v=YAqjf7to-lU)
- [ ] [Youtube: Simple and Efficient Deep Learning for Natural Language Processing, with Moshe Wasserblat, Intel AI](https://www.youtube.com/watch?v=Bgr684dPJ6U)
- [ ] [Youtube: Why not solve biological problems with a Transformer? BERTology meets Biology](https://www.youtube.com/watch?v=pFf4PltQ9LY)
- [ ] [Youtube: Introduction to NLP](https://www.youtube.com/playlist?list=PLM8wYQRetTxCCURc1zaoxo9pTsoov3ipY)
    - [ ] [Introduction to NLP | Bag of Words Model](https://www.youtube.com/watch?v=8Mlc4-3tgzc) `0:22:23`
    - [ ] [Introduction to NLP | TF-IDF](https://www.youtube.com/watch?v=aOIHiclLDrc) `0:10:55`
    - [ ] [Introduction to NLP | Text Cleaning and Preprocessing](https://www.youtube.com/watch?v=p6yvuST_6oQ) `0:14:02`
    - [ ] [Introduction to NLP | Word Embeddings & Word2Vec Model](https://www.youtube.com/watch?v=_Rt4LjasO34) `0:23:09`
    - [ ] [Introduction to NLP | GloVe Model Explained](https://www.youtube.com/watch?v=Fn_U2OG1uqI) `0:23:15`
    - [ ] [Introduction to NLP | GloVe & Word2Vec Transfer Learning](https://www.youtube.com/watch?v=oMd7sMlxYFk) `0:21:12`
    - [ ] [Introduction to NLP | How to Train Custom Word Vectors](https://www.youtube.com/watch?v=-Y_tldJX9jk) `0:13:48`
    - [ ] [Sarcasm is Very Easy to Detect! GloVe + LSTM](https://www.youtube.com/watch?v=pMjT8GIX0co) `0:17:07`
    - [ ] [Text Summarization & Keyword Extraction | Introduction to NLP](https://www.youtube.com/watch?v=XO97Uon83Os) `0:14:59`
- [ ] [Youtube: Self-attention step-by-step | How to get meaning from text](https://youtu.be/-9vVhYEXeyQ)
- [ ] [Youtube: Chat Bot with PyTorch](https://www.youtube.com/playlist?list=PLqnslRFeH2UrFW4AUgn-eY37qOAWQpJyg)
- [ ] [Youtube: NLP with Friends Talks](https://www.youtube.com/playlist?list=PL0zsOCvKa2iEqmPV6WGhjuP-tsrUy102C)
    - [ ] [NLP with Friends, Featured Friend: Tom McCoy](https://www.youtube.com/watch?v=2w1jZyLHzsc) `0:36:48`
    - [ ] [NLP with Friends, Featured Friend: Maarten Sap](https://www.youtube.com/watch?v=1bSk00tEpaM) `0:36:11`
    - [ ] [NLP with Friends, featured friend: Nitika Mathur](https://www.youtube.com/watch?v=w4zyfZV5Q8I) `1:01:42`
    - [ ] [NLP with Friends, Featured Friend: Sabrina J Mielke](https://www.youtube.com/watch?v=4-ulM2moEWg) `1:01:28`
- [ ] [Youtube: Insincere Question Classification with PyTorch](https://www.youtube.com/playlist?list=PLUH_l3HbfEW3Nyst9FTbPiRosViWQRZDL&app=desktop)
    - [ ] [[PART 1] Insincere Question Classification with PyTorch](https://www.youtube.com/watch?v=mmUttpXu7oE) `0:22:34`
    - [ ] [[PART 2] Insincere Question Classification with PyTorch](https://www.youtube.com/watch?v=m3JGRoNSdqE) `0:33:53`
    - [ ] [[PART 3] Insincere Question Classification with PyTorch](https://www.youtube.com/watch?v=-bfYCBznJ6s) `0:23:49`
    - [ ] [[PART 4] Insincere Question Classification with PyTorch](https://www.youtube.com/watch?v=e9CxGeQZTVA) `0:14:25`
- [ ] [Crash Course: Linguistics](https://www.youtube.com/playlist?list=PL8dPuuaLjXtP5mp25nStsuDzk2blncJDW)
    - [ ] [Crash Course Linguistics Preview](https://www.youtube.com/watch?v=eDop3FDoUzk) `0:02:50`
    - [ ] [What is Linguistics?: Crash Course Linguistics #1](https://www.youtube.com/watch?v=3yLXNzDUH58) `0:11:11`
- [ ] [Youtube: Recent Advances in Language Pretraining and Generation](https://docs.google.com/presentation/d/1_a232oAGzI6yRFoZTD_cwRTY8ryL8VIud13s0oNDQhA/mobilepresent?slide=id.g7ec93c1fdb_0_2269)
- [ ] [Youtube: Talks # 3: Lorenzo Ampil - Introduction to T5 for Sentiment Span Extraction](https://www.youtube.com/watch?v=4LYw_UIdd4A)
- [ ] [Youtube: Frontiers in ML: Learning from Limited Labeled Data: Challenges and Opportunities for NLP](https://www.youtube.com/watch?v=MD2fYFt5_5E)
- [ ] [Youtube: DeepLearning.ai NLP talk: Chris Manning](https://www.youtube.com/watch?v=H343JRrncfc&feature=youtu.be)
- [ ] [Youtube: DeepLearning.ai NLP talk: Oren Etzioni](https://www.youtube.com/watch?v=PiF2Aln-L3w&feature=youtu.be)
- [ ] [Youtube: DeepLearning.ai NLP talk: Quoc Le](https://www.youtube.com/watch?v=KGI7K_ehHsU)
- [ ] [Youtube: What can MIR learn from transfer learning in NLP?](https://www.youtube.com/watch?v=9ufD9Who00Y&t=13m26s)
- [ ] [Youtube: The Narrated Transformer Language Model](https://www.youtube.com/watch?v=-QH8fRhqFHM)
- [ ] [Youtube: spaCy v3.0: Bringing State-of-the-art NLP from Prototype to Production](https://globalai.live/october-sessions-natural-language-processing/keynote-2/) `00:22:40`
- [ ] [Youtube: Conversational AI with Transformers and Rule-Based Systems](https://www.youtube.com/watch?v=_-rCUnPBlME) `1:53:24`
- [ ] [Talk: High Performance Natural Language Processing](https://slideslive.com/38940826)
- [ ] [Talk: EmoTag1200: Understanding the Association between Emojis and Emotions](https://slideslive.com/38939357/emotag1200-understanding-the-association-between-emojis-and-emotions)
- [ ] [Youtube: Research Paper Walkthrough](https://www.youtube.com/playlist?list=PLsAqq9lZFOtWUz1WEoJ3GXw197LD7BxMc)
    - [ ] [Simple Unsupervised Keyphrase Extraction using Sentence Embeddings | Research Paper Walkthrough](https://www.youtube.com/watch?v=ykClwtoLER8) `0:21:23`
    - [ ] [Leveraging BERT for Extractive Text Summarization on Lectures | Research Paper Walkthrough](https://www.youtube.com/watch?v=JU6eSLsp6vI) `0:20:10`
    - [ ] [Data Augmentation Techniques for Text Classification in NLP | Research Paper Walkthrough](https://www.youtube.com/watch?v=-1unNLkwImw) `0:14:33`
    - [ ] [CRIM at SemEval-2018 Task 9: A Hybrid Approach to Hypernym Discovery | Research Paper Walkthrough](https://www.youtube.com/watch?v=AxMSd4EPVVc) `0:23:47`
    - [ ] [Data Augmentation using Pre-trained Transformer Model (BERT, GPT2, etc) | Research Paper Walkthrough](https://www.youtube.com/watch?v=9O9scQb4sNo) `0:17:43`
    - [ ] [A Supervised Approach to Extractive Summarisation of Scientific Papers | Research Paper Walkthrough](https://www.youtube.com/watch?v=73uWfopdjoc) `0:19:01`
    - [ ] [BLEURT: Learning Robust Metrics for Text Generation | Research Paper Walkthrough](https://www.youtube.com/watch?v=9lWxwfMKAdM) `0:13:38`
    - [ ] [DeepWalk: Online Learning of Social Representations | ML with Graphs | Research Paper Walkthrough](https://www.youtube.com/watch?v=-uJL_ANy1jc) `0:17:44`
    - [ ] [LSBert: A Simple Framework for Lexical Simplification | Research Paper Walkthrough](https://www.youtube.com/watch?v=uhnKsGDyhEg) `0:20:27`
    - [ ] [SpanBERT: Improving Pre-training by Representing and Predicting Spans | Research Paper Walkthrough](https://www.youtube.com/watch?v=QUP3rMrA1mk) `0:14:21`
    - [ ] [Text Summarization of COVID-19 Medical Articles using BERT and GPT-2 | Research Paper Walkthrough](https://www.youtube.com/watch?v=kC5kP1dPAzc) `0:21:52`
    - [ ] [Extractive & Abstractive Summarization with Transformer Language Models | Research Paper Walkthrough](https://www.youtube.com/watch?v=2IzXW3Ypks0) `0:16:58`
    - [ ] [Unsupervised Multi-Document Summarization using Neural Document Model | Research Paper Walkthrough](https://www.youtube.com/watch?v=qOoAlI5hpFk) `0:15:11`
    - [ ] [SummPip: Multi-Document Summarization with Sentence Graph Compression | Research Paper Walkthrough](https://www.youtube.com/watch?v=1jwUOMQVCo4) `0:16:54`
    - [ ] [Combining BERT with Static Word Embedding for Categorizing Social Media | Research Paper Walkthrough](https://www.youtube.com/watch?v=VqlA_ALWQdM) `0:13:51`
    - [ ] [Reformulating Unsupervised Style Transfer as Paraphrase Generation | Research Paper Walkthrough](https://www.youtube.com/watch?v=cjnk3PJljDs) `0:19:41`
    - [ ] [PEGASUS: Pre-training with Gap-Sentences for Abstractive Summarization | Research Paper Walkthrough](https://www.youtube.com/watch?v=QY8oZxS0txs) `0:15:04`
    - [ ] [Evaluation of Text Generation: A Survey | Human-Centric Evaluations | Research Paper Walkthrough](https://www.youtube.com/watch?v=-CIlz-5um7U) `0:15:54`
	- [X] [TOD-BERT: Pre-trained Transformers for Task-Oriented Dialogue Systems (Research Paper Walkthrough)](https://www.youtube.com/watch?v=z3Pe0cJUvO0) `0:15:25`
	- [ ] [TextRank: Bringing Order into Texts (Research Paper Walkthrough)](https://www.youtube.com/watch?v=2l6Fa767kEw) `0:14:34`
	- [ ] [Node2Vec: Scalable Feature Learning for Networks | ML with Graphs (Research Paper Walkthrough)](https://www.youtube.com/watch?v=LpwGZG5j_q0) `0:14:33`
	- [ ] [HARP: Hierarchical Representation Learning for Network | ML with Graphs (Research Paper Walkthrough)](https://www.youtube.com/watch?v=7HJFa8Xct80) `0:15:10`
	- [ ] [URL2Video: Automatic Video Creation From a Web Page | AI and Creativity (Research Paper Walkthrough)](https://www.youtube.com/watch?v=SlPvzvoU3a8) `0:15:21`
	- [ ] [On Generating Extended Summaries of Long Documents (Research Paper Walkthrough)](https://www.youtube.com/watch?v=Inc63mLLInA) `0:14:24`
	- [ ] [Nucleus Sampling: The Curious Case of Neural Text Degeneration (Research Paper Walkthrough)](https://www.youtube.com/watch?v=dCORspO2yVY) `0:12:48`
	- [ ] [T5: Exploring Limits of Transfer Learning with Text-to-Text Transformer (Research Paper Walkthrough)](https://www.youtube.com/watch?v=91iLu6OOrwk) `0:12:47`
	- [ ] [DialoGPT: Generative Training for Conversational Response Generation (Research Paper Walkthrough)](https://www.youtube.com/watch?v=Zo679MYoJns) `0:13:17`
	- [ ] [Hierarchical Transformers for Long Document Classification (Research Paper Walkthrough)](https://www.youtube.com/watch?v=3IOl5d9PZeM) `0:12:46`
	- [ ] [Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (Best Paper ACL 2020)](https://www.youtube.com/watch?v=m4EtNuR5RFQ) `0:14:00`
- [ ] [NLP Summit 2020](https://www.youtube.com/playlist?list=PL5zieHHAlvAr2QkDyP-BfEVmaIC5BAq4q)
    - [ ] [The 2020 Trends for Applied Natural Language Processing | NLP Summit 2020](https://www.youtube.com/watch?v=bAPVJJIPMy0) `0:21:10`
    - [ ] [NLP Industry Survey Analysis: the landscape of natural language use cases in 2020 | NLP Summit 2020](https://www.youtube.com/watch?v=93BJyr60DIE) `0:20:23`
    - [ ] [Auto NLP: Pretrain, Tune & Deploy State-of-the-art Models Without Coding](https://www.youtube.com/watch?v=-ud0zz2ttwU) `0:19:57`
    - [ ] [Proof-of-Concept Delight | NLP Summit 2020](https://www.youtube.com/watch?v=gWap5pmzye0) `0:16:50`
    - [ ] [Distributed Natural Language Processing Apps for Financial Engineering | NLP Summit 2020](https://www.youtube.com/watch?v=SZJPV6QLmTI) `0:34:49`
    - [ ] [Bleeding Edge Applications of 2020 Transformers | NLP Summit 2020](https://www.youtube.com/watch?v=C1-408G3WO8) `0:33:34`
    - [ ] [How Freshworks Freddy AI leverages NLP for Ethics-First Customer Experiences | NLP Summit 2020](https://www.youtube.com/watch?v=FxG92XXbnD8) `0:26:49`
    - [ ] [NLP for Recruitment Automation: Building a Chatbot from the Job Description | NLP Summit 2020](https://www.youtube.com/watch?v=i6-RMbE_auQ) `0:22:31`
- [ ] [Youtube: Explainability for Natural Language Processing](https://www.youtube.com/watch?v=3tnrGe_JA0s)
- [ ] [Youtube: Gibberish Detector](https://www.youtube.com/watch?v=l15C8UJu17s)
- [ ] [Youtube: NLP Lecture 7 Constituency Parsing](https://www.youtube.com/playlist?list=PLr9TFf9GjancAHiiP5cqGmYsZad99Uf_h)
	- [ ] [NLP Lecture 7 - Overview of Constituency Parsing Lecture](https://www.youtube.com/watch?v=pXTrY2GPMn0) `0:01:50`
	- [ ] [NLP Lecture 7 - Introduction to Constituency Parsing](https://www.youtube.com/watch?v=El4r91dgCTQ) `0:10:29`
	- [ ] [NLP Lecture 7(a) - Context Free Grammar](https://www.youtube.com/watch?v=kq4aUYzLlb0) `0:17:03`
	- [ ] [NLP Lecture 7(b) - Constituency Parsing](https://www.youtube.com/watch?v=_OpOoiySZRA) `0:13:28`
	- [ ] [NLP Lecture 7(c) - Statistical Constituency Parsing](https://www.youtube.com/watch?v=avmq_oVGOOM) `0:09:38`
	- [ ] [NLP Lecture 7(d) - Dependency Parsing](https://www.youtube.com/watch?v=2jLk93iIyrw) `0:17:15`
- [ ] [Youtube: LING 83 Teaching Video: Constituency Parsing](https://www.youtube.com/watch?v=G_bd2z4mIiI)
- [ ] [Youtube: SpaCy for Digital Humanities with Python Tutorials](https://www.youtube.com/playlist?list=PL2VXyKi-KpYvuOdPwXR-FZfmZ0hjoNSUo)
	- [ ] [Introduction to SpaCy and Cleaning Data (SpaCy and Python Tutorials for DH - 01)](https://www.youtube.com/watch?v=8HZ4BjWMod4) `0:06:07`
	- [ ] [How to Install SpaCy and Models (Spacy and Python Tutorial for DH 02)](https://www.youtube.com/watch?v=yqruv_QQctI) `0:07:40`
	- [ ] [How to Separate Sentences in SpaCy (SpaCy and Python Tutorials for DH - 03)](https://www.youtube.com/watch?v=ytAyCO-n8tY) `0:08:33`
	- [ ] [Spacy and Named Entity Recognition NER (Spacy and Python Tutorial for DH 04)](https://www.youtube.com/watch?v=lxHNsXudkrY) `0:08:32`
	- [ ] [Finding Parts of Speech (SpaCy and Python Tutorial for DH 05)](https://www.youtube.com/watch?v=nv0pksknFxY) `0:02:55`
	- [ ] [Extracting Nouns and Noun Chunks (SpaCy and Python Tutorial for DH 06)](https://www.youtube.com/watch?v=aNKt1gKK8Lo) `0:05:46`
	- [ ] [Extracting Verbs and Verb Phrases (SpaCy and Python Tutorial for DH 07)](https://www.youtube.com/watch?v=VgGHwIWu-kU) `0:08:10`
	- [ ] [Lemmatization: Finding the Roots of Words (Spacy and Python Tutorial for DH 08)](https://www.youtube.com/watch?v=YztOLsJkC3A) `0:04:52`
	- [ ] [Data Visualization with DisplaCy (Spacy and Python Tutorial for DH 09)](https://www.youtube.com/watch?v=UYOq3LXoka8) `0:09:13`
	- [ ] [Customizing DisplaCy Render Data Visualization (Spacy and Python Tutorial for DH 10)](https://www.youtube.com/watch?v=FAgMcg9hxkU) `0:08:19`
	- [ ] [Finding Quotes in Sentences (SpaCy and Python Tutorial for DH 11)](https://www.youtube.com/watch?v=wZE4YmtEBe0) `0:08:45`
	- [ ] [Introduction to Named Entity Recognition (NER for DH 01)](https://www.youtube.com/watch?v=E9h8qVm2uNY) `0:16:43`
	- [ ] [Machine Learning NER with Python and spaCy (NER for DH 03 )](https://www.youtube.com/watch?v=2Ny0yATnuxY) `0:13:36`
	- [ ] [How to Use spaCy's EntityRuler (Named Entity Recognition for DH 04 | Part 01)](https://www.youtube.com/watch?v=wpyCzodvO3A) `0:36:50`
	- [ ] [How to Use spaCy to Create an NER training set (Named Entity Recognition for DH 04 | Part 02)](https://www.youtube.com/watch?v=YBRF7tq1V-Q) `0:10:32`
	- [ ] [How to Train a spaCy NER model (Named Entity Recognition for DH 04 | Part 03)](https://www.youtube.com/watch?v=7Z1imsp6g10) `0:15:40`
	- [ ] [Examining a spaCy Model in the Folder (Named Entity Recognition for DH 05)](https://www.youtube.com/watch?v=cuUf2H6uCA8) `0:15:06`
	- [ ] [What are Word Vectors (Named Entity Recognition for DH 06)](https://www.youtube.com/watch?v=eZJm7PisZvk) `0:18:49`
	- [ ] [How to Generate Custom Word Vectors in Gensim (Named Entity Recognition for DH 07)](https://www.youtube.com/watch?v=6HM75qOsgkU) `0:23:05`
	- [ ] [How to Load Custom Word Vectors into spaCy Models (Named Entity Recognition for DH 08)](https://www.youtube.com/watch?v=aQPMWS6XiI8) `0:10:46`
	- [ ] [Getting the Data for Custom Labels (Holocaust NER for DH 09.01)](https://www.youtube.com/watch?v=XScpSI3RYQI) `0:11:00`
	- [ ] [How to Add a Custom NER Pipe in spaCy and a Custom Label (NER for DH 09.02 )](https://www.youtube.com/watch?v=1l3v2Zcgb3s) `0:07:49`
	- [ ] [How to Training Custom Entities into spaCy Models (Named Entity Recognition for DH 09 03)](https://www.youtube.com/watch?v=EcwehOuoqxw) `0:15:29`
	- [ ] [How to Add and Place Pipes from other Models into a New Model (NER for DH 09 04)](https://www.youtube.com/watch?v=fNvTnf10N2M) `0:12:24`
	- [ ] [How to Add Custom Functions to spaCy Pipeline (NER for DH 09.05)](https://www.youtube.com/watch?v=XE22cAnwmuQ) `0:15:20`
	- [ ] [Precision vs. Recall and Adding PERSON to Holocaust NER Pipeline (Named Entity Recognition DH 09.06)](https://www.youtube.com/watch?v=-321RJ_Rna8) `0:26:02`
	- [ ] [Finalizing the Holocaust NER Pipeline (Named Entity Recognition for DH 09.07)](https://www.youtube.com/watch?v=MYs0W2fPxSo) `0:14:16`
	- [ ] [Classical Latin Named Entity Recognition (NER for DH 10.01)](https://www.youtube.com/watch?v=zywMBYmV6Ew) `0:55:30`
	- [ ] [How to Package spaCy Models (Even with Custom Factories) (NER for DH 11)](https://www.youtube.com/watch?v=AGM7lXfQECM) `0:15:31`
- [ ] [Youtube: Billion-scale Approximate Nearest Neighbor Search](https://www.youtube.com/watch?v=SKrHs03i08Q)
- [ ] [Youtube: Data Science - Fuzzy Record Matching](https://www.youtube.com/watch?v=YhrKvEjpBYo)
- [ ] [Youtube: Minimum Edit Distance Dynamic Programming](https://www.youtube.com/watch?v=We3YDTzNXEk)
- [ ] [Youtube: Cheuk Ting Ho - Fuzzy Matching Smart Way of Finding Similar Names Using Fuzzywuzzy](https://www.youtube.com/watch?v=NRAqIjXaZvw)
- [ ] [Youtube: What's in a Name? Fast Fuzzy String Matching - Seth Verrinder & Kyle Putnam - Midwest.io 2015](https://www.youtube.com/watch?v=s0YSKiFdj8Q)
- [ ] [Youtube: Jiaqi Liu Fuzzy Search Algorithms How and When to Use Them PyCon 2017](https://www.youtube.com/watch?v=kTS2b6pGElE)
- [ ] [Youtube: 1 + 1 = 1 or Record Deduplication with Python | Flávio Juvenal @ PyBay2018](https://www.youtube.com/watch?v=McsTWXeURhA)
- [ ] [Youtube: Mike Mull: The Art and Science of Data Matching](https://www.youtube.com/watch?v=Y-nYEOgq3YE)
- [ ] [Youtube: Record linkage: Join for real life by Rhydwyn Mcguire](https://youtu.be/cEcVIjyHfiQ)
- [ ] [Youtube: Approximate nearest neighbors and vector models, introduction to Annoy](https://www.youtube.com/watch?v=QkCCyLW0ehU)
- [ ] [Youtube: Librosa Audio and Music Signal Analysis in Python | SciPy 2015 | Brian McFee](https://www.youtube.com/watch?v=MhOdbtPhbLU)
- [ ] [Video: Recent Advances in LM Pre-training](https://slideslive.com/38952401/recent-advances-in-lm-pretraining)
- [ ] [Youtube: Deep Learning (for Audio) with Python](https://www.youtube.com/playlist?list=PL-wATfeyAMNrtbkCNsLcpoAyBBRJZVlnf)
	- [ ] [1- Deep Learning (for Audio) with Python: Course Overview](https://www.youtube.com/watch?v=fMqL5vckiU0) `0:08:02`
	- [ ] [2- AI, machine learning and deep learning](https://www.youtube.com/watch?v=1LLxZ35ru_g) `0:31:15`
	- [ ] [3- Implementing an artificial neuron from scratch](https://www.youtube.com/watch?v=qxIaW-WvLDU) `0:19:05`
	- [ ] [4- Vector and matrix operations](https://www.youtube.com/watch?v=FmD1S5yP_os) `0:25:51`
	- [ ] [5-  Computation in neural networks](https://www.youtube.com/watch?v=QUCzvlgvk6I) `0:23:19`
	- [ ] [6- Implementing a neural network from scratch in Python](https://www.youtube.com/watch?v=0oWnheK-gGk) `0:21:03`
	- [ ] [7- Training a neural network: Backward propagation and gradient descent](https://www.youtube.com/watch?v=ScL18goxsSg) `0:21:41`
	- [ ] [8- TRAINING A NEURAL NETWORK: Implementing backpropagation and gradient descent from scratch](https://www.youtube.com/watch?v=Z97XGNUUx9o) `1:03:00`
	- [ ] [9- How to implement a (simple) neural network with TensorFlow 2](https://www.youtube.com/watch?v=JdXxaZcQer8) `0:24:37`
	- [ ] [10 - Understanding audio data for deep learning](https://www.youtube.com/watch?v=m3XbqfIij_Y) `0:32:55`
	- [ ] [11- Preprocessing audio data for Deep Learning](https://www.youtube.com/watch?v=Oa_d-zaUti8) `0:25:05`
	- [ ] [12- Music genre classification: Preparing the dataset](https://www.youtube.com/watch?v=szyGiObZymo) `0:37:45`
	- [ ] [13- Implementing a neural network for music genre classification](https://www.youtube.com/watch?v=_xcFAiufwd0) `0:33:25`
	- [ ] [14-  SOLVING OVERFITTING in neural networks](https://www.youtube.com/watch?v=Gf5DO6br0ts) `0:26:29`
	- [ ] [15- Convolutional Neural Networks Explained Easily](https://www.youtube.com/watch?v=t3qWfUYJEYU) `0:35:23`
	- [ ] [16- How to Implement a CNN for Music Genre Classification](https://www.youtube.com/watch?v=dOG-HxpbMSw) `0:49:10`
	- [ ] [17- Recurrent Neural Networks Explained Easily](https://www.youtube.com/watch?v=DY82Goknf0s) `0:28:35`
	- [ ] [18- Long Short Term Memory (LSTM) Networks Explained Easily](https://www.youtube.com/watch?v=eCvz-kB4yko) `0:28:08`
	- [ ] [19- How to Implement an RNN-LSTM Network for Music Genre Classification](https://www.youtube.com/watch?v=4nXI0h2sq2I) `0:14:29`
- [ ] [Youtube: Fine-tuning a large language model without your own supercomputer](https://www.youtube.com/watch?v=RG-yV5zgqjQ)

### Be familiar with Recommendation Systems
- [ ] [Google: Recommendation Systems](https://developers.google.com/machine-learning/recommendation)
- [ ] [Pluralsight: Understanding Algorithms for Recommendation Systems](https://www.pluralsight.com/courses/algorithms-recommendation-systems)
- [ ] [Youtube: Learning to Rank: From Theory to Production - Malvina Josephidou & Diego Ceccarelli, Bloomberg](https://youtu.be/eMuepJpjUjI)
- [ ] [Youtube: Learning "Learning to Rank"](https://youtu.be/7teudGhdnqo)
- [ ] [Youtube: Learning to rank search results - Byron Voorbach & Jettro Coenradie [DevCon 2018]](https://youtu.be/TG7aNLgzIcM)

### Be able to implement computer vision models
- [ ] [Article: The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)](https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html)
- [ ] [Article: How to extract Key-Value pairs from Documents using deep learning](https://nanonets.com/blog/key-value-pair-extraction-from-documents-using-ocr-and-deep-learning/)
- [ ] [Article: Essential Pil (Pillow) Image Tutorial (for Machine Learning People)](https://neptune.ai/blog/pil-image-tutorial-for-machine-learning)
- [ ] [Article: What is Focal Loss and when should you use it?](https://amaarora.github.io/2020/06/29/FocalLoss.html)
- [ ] [Article: Part 1: Deep Representations, a way towards neural style transfer](https://wandb.ai/authors/nerual_style_transfer/reports/Part-1-Deep-Representations-a-way-towards-neural-style-transfer--VmlldzoyMjQzNDY)
- [ ] [Article: A gentle introduction to OCR](https://towardsdatascience.com/a-gentle-introduction-to-ocr-ee1469a201aa)
- [ ] [Article: Breaking Linear Classifiers on ImageNet](http://karpathy.github.io/2015/03/30/breaking-convnets/)
- [ ] [Article: Building an image search service from scratch](https://mlpowered.com/posts/image-search/)
- [ ] [Article: Squeeze and Excitation Networks Explained with PyTorch Implementation](https://amaarora.github.io/2020/07/24/SeNet.html)
- [ ] [Article: Multimodal Neurons in Artificial Neural Networks](https://openai.com/blog/multimodal-neurons/)
- [ ] [Article: DenseNet Architecture Explained with PyTorch Implementation from TorchVision](https://amaarora.github.io/2020/08/02/densenets.html)
- [ ] [Article: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://amaarora.github.io/2020/08/13/efficientnet.html)
- [ ] [Article: Group Normalization](https://amaarora.github.io/2020/08/09/groupnorm.html)
- [ ] [Article: A Short Introduction to Generative Adversarial Networks](https://sthalles.github.io/semi-supervised-learning-with-gans/)
- [ ] [Article: Semi-supervised Learning with GANs](https://sthalles.github.io/intro-to-gans/)
- [ ] [Article: Densely Connected Convolutional Networks in Tensorflow](https://sthalles.github.io/densely-connected-conv-nets/)
- [ ] [Article: Convolutional neural networks](https://www.jeremyjordan.me/convolutional-neural-networks/)
- [ ] [Article: Common architectures in convolutional neural networks](https://www.jeremyjordan.me/convnet-architectures/)
- [ ] [Article: An overview of semantic image segmentation](https://www.jeremyjordan.me/semantic-segmentation/)
- [ ] [Article: Evaluating image segmentation models](https://www.jeremyjordan.me/evaluating-image-segmentation-models/)
- [ ] [Article: An overview of object detection: one-stage methods](https://www.jeremyjordan.me/object-detection-one-stage/)
- [ ] [Article: A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN](https://blog.athelas.com/a-brief-history-of-cnns-in-image-segmentation-from-r-cnn-to-mask-r-cnn-34ea83205de4)
- [ ] [Article: Object Detection for Dummies Part 1: Gradient Vector, HOG, and SS](https://lilianweng.github.io/lil-log/2017/10/29/object-recognition-for-dummies-part-1.html)
- [ ] [Article: Object Detection for Dummies Part 2: CNN, DPM and Overfeat](https://lilianweng.github.io/lil-log/2017/12/15/object-recognition-for-dummies-part-2.html)
- [ ] [Article: Object Detection for Dummies Part 3: R-CNN Family](https://lilianweng.github.io/lil-log/2017/12/31/object-recognition-for-dummies-part-3.html)
- [ ] [Article: Understanding coordinate systems and DICOM for deep learning medical image analysis](https://theaisummer.com/medical-image-coordinates/)
- [ ] [Article: Understanding the receptive field of deep convolutional networks](https://theaisummer.com/receptive-field/)
- [ ] [Article: Deep learning in medical imaging - 3D medical image segmentation with PyTorch](https://theaisummer.com/medical-image-deep-learning/)
- [ ] [Article: Intuitive Explanation of Skip Connections in Deep Learning](https://theaisummer.com/skip-connections/)
- [ ] [Article: Human Pose Estimation](https://theaisummer.com/Human-Pose-Estimation/)
- [ ] [Article: YOLO - You only look once (Single shot detectors)](https://theaisummer.com/YOLO/)
- [ ] [Article: Localization and Object Detection with Deep Learning](https://theaisummer.com/Localization_and_Object_Detection/)
- [ ] [Article: Semantic Segmentation in the era of Neural Networks](https://theaisummer.com/Semantic_Segmentation/)
- [ ] [Article: ECCV 2020: Some Highlights](https://yassouali.github.io/ml-blog/eccv2020/)
- [ ] [Article: NonCompositional](https://fh295.github.io/noncompositional.html)
- [ ] [Article: Looking Inside The Blackbox — How To Trick A Neural Network](https://towardsdatascience.com/peering-inside-the-blackbox-how-to-trick-a-neural-network-757c90a88a73)
- [ ] [AWS: Semantic Segmentation Explained](https://www.aws.training/learningobject/video?id=27238)
- [ ] [Book: Deep Learning for Computer Vision with Python](https://www.pyimagesearch.com/deep-learning-computer-vision-python-book/)
- [ ] [Book: Practical Python and OpenCV](https://www.pyimagesearch.com/practical-python-opencv/)
- [ ] [Coursera: Convolutional Neural Networks](https://www.coursera.org/learn/convolutional-neural-networks?specialization=deep-learning)
- [ ] [Datacamp: Biomedical Image Analysis in Python](https://www.datacamp.com/courses/biomedical-image-analysis-in-python)
- [ ] [Datacamp: Image Processing in Python](https://www.datacamp.com/courses/image-processing-in-python)
- [ ] [Google: ML Practicum: Image Classification](https://developers.google.com/machine-learning/practica/image-classification)
- [ ] [Stanford: CS231N Winter 2016](https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC)
	- [ ] [CS231n Winter 2016: Lecture 1: Introduction and Historical Context](https://www.youtube.com/watch?v=NfnWJUyUJYU) `1:19:08`
	- [ ] [CS231n Winter 2016: Lecture 2: Data-driven approach, kNN, Linear Classification 1](https://www.youtube.com/watch?v=8inugqHkfvE) `0:57:28`
	- [ ] [CS231n Winter 2016: Lecture 3: Linear Classification 2, Optimization](https://www.youtube.com/watch?v=qlLChbHhbg4) `1:11:23`
	- [ ] [CS231n Winter 2016: Lecture 4: Backpropagation, Neural Networks 1](https://www.youtube.com/watch?v=i94OvYb6noo) `1:19:38`
	- [ ] [CS231n Winter 2016: Lecture 5: Neural Networks Part 2](https://www.youtube.com/watch?v=gYpoJMlgyXA) `1:18:37`
	- [ ] [CS231n Winter 2016: Lecture 6: Neural Networks Part 3 / Intro to ConvNets](https://www.youtube.com/watch?v=hd_KFJ5ktUc) `1:09:35`
	- [ ] [CS231n Winter 2016: Lecture 7: Convolutional Neural Networks](https://www.youtube.com/watch?v=LxfUGhug-iQ) `1:19:01`
	- [ ] [CS231n Winter 2016: Lecture 8: Localization and Detection](https://www.youtube.com/watch?v=GxZrEKZfW2o) `1:04:57`
	- [ ] [CS231n Winter 2016: Lecture 9: Visualization, Deep Dream, Neural Style, Adversarial Examples](https://www.youtube.com/watch?v=ta5fdaqDT3M) `1:18:20`
	- [ ] [CS231n Winter 2016: Lecture 10: Recurrent Neural Networks, Image Captioning, LSTM](https://www.youtube.com/watch?v=yCC09vCHzF8) `1:09:54`
	- [ ] [CS231n Winter 2016: Lecture 11: ConvNets in practice](https://www.youtube.com/watch?v=pA4BsUK3oP4) `1:15:03`
	- [ ] [CS231n Winter 2016: Lecture 12: Deep Learning libraries](https://www.youtube.com/watch?v=Vf_-OkqbwPo) `1:21:06`
	- [ ] [CS231n Winter 2016: Lecture 14: Videos and Unsupervised Learning](https://www.youtube.com/watch?v=ekyBklxwQMU) `1:17:36`
	- [ ] [CS231n Winter 2016: Lecture 13: Segmentation, soft attention, spatial transformers](https://www.youtube.com/watch?v=ByjaPdWXKJ4) `1:10:59`
	- [ ] [CS231n Winter 2016: Lecture 15: Invited Talk by Jeff Dean](https://www.youtube.com/watch?v=T7YkPWpwFD4) `1:14:49`
- [ ] [Udacity: Introduction to Computer Vision](https://www.udacity.com/course/introduction-to-computer-vision--ud810)
- [ ] [Youtube: Deep Residual Learning for Image Recognition (Paper Explained)](https://www.youtube.com/watch?v=GWt6Fu05voI)
- [ ] [Youtube: Implementing ResNet from scratch](https://www.youtube.com/playlist?list=PLbMqOoYQ3MxywF4R6MOJO7i9jFEeSGSSC)
- [ ] [Youtube: ConvNets Scaled Efficiently](https://www.youtube.com/watch?v=fC39F8AqPo0) `0:13:19`
- [ ] [Youtube: Building an Image Captioner with Neural Networks](https://www.youtube.com/watch?v=c_bVBYxX5EU) `0:12:54`
- [ ] [Youtube: Evolution of Face Generation |  Evolution of GANs](https://www.youtube.com/watch?v=C1YUYWP-6rE) `0:12:23`
- [ ] [Youtube: Autoencoders - EXPLAINED](https://www.youtube.com/watch?v=7mRfwaGGAPg) `0:10:53`
- [ ] [Youtube: Unpaired Image-Image Translation using CycleGANs](https://www.youtube.com/watch?v=NyAosnNQv_U) `0:16:22`
- [ ] [Youtube: AI creates Image Classifiers…by DRAWING?](https://www.youtube.com/watch?v=BeYbQkbKox8) `0:09:04`
- [ ] [Youtube: The Evolution of Convolution Neural Networks](https://www.youtube.com/watch?v=Y2Tna77k1aI) `0:24:02`
- [ ] [Youtube: Depthwise Separable Convolution - A FASTER CONVOLUTION!](https://www.youtube.com/watch?v=T7o3xvJLuHk) `0:12:43`
- [ ] [Youtube: Mask Region based Convolution Neural Networks - EXPLAINED!](https://www.youtube.com/watch?v=4tkgOzQ9yyo) `0:09:34`
- [ ] [Youtube: Sound play with Convolution Neural Networks](https://www.youtube.com/watch?v=GNza2ncnMfA) `0:11:57`
- [ ] [Youtube: Convolution Neural Networks - EXPLAINED](https://www.youtube.com/watch?v=m8pOnJxOcqY) `0:19:20`
- [ ] [Youtube: Generative Adversarial Networks - FUTURISTIC & FUN AI !](https://www.youtube.com/watch?v=O8LAi6ksC80) `0:14:20`
- [ ] [Youtube: How Convolution Works](https://www.youtube.com/watch?v=B-M5q51U8SM&feature=youtu.be)
- [ ] [Youtube: DETR: End-to-End Object Detection with Transformers (Paper Explained)](https://www.youtube.com/watch?v=T35ba_VXkMY)
- [ ] [Youtube: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained)](https://www.youtube.com/watch?v=TrdevFK_am4)

### Be able to model graphs and network data
- [ ] [Datacamp: Network Analysis in Python (Part 1)](https://www.datacamp.com/courses/network-analysis-in-python-part-1)
- [ ] [Datacamp: Network Analysis in Python (Part 2)](https://www.datacamp.com/courses/network-analysis-in-python-part-2)

### Be able to implement models for timeseries and forecasting
- [ ] [Datacamp: Machine Learning for Finance in Python](https://www.datacamp.com/courses/machine-learning-for-finance-in-python)
- [ ] [Datacamp: Introduction to Time Series Analysis in Python](https://www.datacamp.com/courses/introduction-to-time-series-analysis-in-python)
- [ ] [Datacamp: Machine Learning for Time Series Data in Python](https://www.datacamp.com/courses/machine-learning-for-time-series-data-in-python)
- [ ] [Datacamp: Intro to Portfolio Risk Management in Python](https://www.datacamp.com/courses/intro-to-portfolio-risk-management-in-python)
- [ ] [Datacamp: Financial Forecasting in Python](https://www.datacamp.com/courses/financial-forecasting-in-python)
- [ ] [Datacamp: Predicting CTR with Machine Learning in Python](https://www.datacamp.com/courses/predicting-ctr-with-machine-learning-in-python)
- [ ] [Datacamp: Intro to Financial Concepts using Python](https://www.datacamp.com/courses/intro-to-financial-concepts-using-python)
- [ ] [Datacamp: Fraud Detection in Python](https://www.datacamp.com/courses/fraud-detection-in-python)
- [ ] [Datacamp: Forecasting Using ARIMA Models in Python](https://www.datacamp.com/courses/forecasting-using-arima-models-in-python)
- [ ] [Datacamp: Introduction to Portfolio Analysis in Python](https://www.datacamp.com/courses/introduction-to-portfolio-analysis-in-python)
- [ ] [Datacamp: Credit Risk Modeling in Python](https://www.datacamp.com/courses/credit-risk-modeling-in-python)
- [ ] [Datacamp: Machine Learning for Marketing in Python](https://www.datacamp.com/courses/machine-learning-for-marketing-in-python)
- [ ] [Udacity: Machine Learning for Trading](https://www.udacity.com/course/machine-learning-for-trading--ud501)
- [ ] [Udacity: Time Series Forecasting](https://www.udacity.com/course/time-series-forecasting--ud980)

### Be familiar with Reinforcement Learning
- [ ] [DeepLizard: Reinforcement Learning - Goal Oriented Intelligence](https://www.youtube.com/playlist?list=PLZbbT5o_s2xoWNVdDudn51XM8lOuZ_Njv)
	- [ ] [Reinforcement Learning Series Intro - Syllabus Overview](https://www.youtube.com/watch?v=nyjbcRQ-uQ8) `0:05:51`
	- [ ] [Markov Decision Processes (MDPs) - Structuring a Reinforcement Learning Problem](https://www.youtube.com/watch?v=my207WNoeyA) `0:06:34`
	- [ ] [Expected Return - What Drives a Reinforcement Learning Agent in an MDP](https://www.youtube.com/watch?v=a-SnJtmBtyA) `0:06:47`
	- [ ] [Policies and Value Functions - Good Actions for a Reinforcement Learning Agent](https://www.youtube.com/watch?v=eMxOGwbdqKY) `0:06:52`
	- [ ] [What do Reinforcement Learning Algorithms Learn - Optimal Policies](https://www.youtube.com/watch?v=rP4oEpQbDm4) `0:06:21`
	- [ ] [Q-Learning Explained - A Reinforcement Learning Technique](https://www.youtube.com/watch?v=qhRNvCVVJaA) `0:08:37`
	- [ ] [Exploration vs. Exploitation - Learning the Optimal Reinforcement Learning Policy](https://www.youtube.com/watch?v=mo96Nqlo1L8) `0:10:06`
	- [ ] [OpenAI Gym and Python for Q-learning - Reinforcement Learning Code Project](https://www.youtube.com/watch?v=QK_PP_2KgGE) `0:07:52`
	- [ ] [Train Q-learning Agent with Python - Reinforcement Learning Code Project](https://www.youtube.com/watch?v=HGeI30uATws) `0:08:59`
	- [ ] [Watch Q-learning Agent Play Game with Python - Reinforcement Learning Code Project](https://www.youtube.com/watch?v=ZaILVnqZFCg) `0:07:22`
	- [ ] [Deep Q-Learning - Combining Neural Networks and Reinforcement Learning](https://www.youtube.com/watch?v=wrBUkpiRvCA) `0:10:50`
	- [ ] [Replay Memory Explained - Experience for Deep Q-Network Training](https://www.youtube.com/watch?v=Bcuj2fTH4_4) `0:06:21`
	- [ ] [Training a Deep Q-Network - Reinforcement Learning](https://www.youtube.com/watch?v=0bt0SjbS3xc) `0:09:07`
	- [ ] [Training a Deep Q-Network with Fixed Q-targets - Reinforcement Learning](https://www.youtube.com/watch?v=xVkPh9E9GfE) `0:07:35`
	- [ ] [Deep Q-Network Code Project Intro - Reinforcement Learning](https://www.youtube.com/watch?v=FU-sNVew9ZA) `0:06:26`
	- [ ] [Build Deep Q-Network - Reinforcement Learning Code Project](https://www.youtube.com/watch?v=PyQNfsGUnQA) `0:16:51`
	- [ ] [Deep Q-Network Image Processing and Environment Management - Reinforcement Learning Code Project](https://www.youtube.com/watch?v=jkdXDinWfo8) `0:21:53`
	- [ ] [Deep Q-Network Training Code - Reinforcement Learning Code Project](https://www.youtube.com/watch?v=ewRw996uevM) `0:19:46`

### Be able to optimize performance metric
- [ ] [A recipe for training neural networks](http://karpathy.github.io/2019/04/25/recipe/)
- [ ] [Article: Evaluating a machine learning model](https://www.jeremyjordan.me/evaluating-a-machine-learning-model/)
- [ ] [Article: Hyperparameter tuning for machine learning models](https://www.jeremyjordan.me/hyperparameter-tuning/)
- [ ] [Article: Hacker's Guide to Hyperparameter Tuning](https://www.curiousily.com/posts/hackers-guide-to-hyperparameter-tuning/)
- [ ] [Article: Environment and Distribution Shift](http://d2l.ai/chapter_multilayer-perceptrons/environment.html#types-of-distribution-shift)
- [ ] [Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization](https://www.coursera.org/learn/deep-neural-network?specialization=deep-learning)
- [ ] [Datacamp: Model Validation in Python](https://www.datacamp.com/courses/model-validation-in-python)
- [ ] [Datacamp: Hyperparameter Tuning in Python](https://www.datacamp.com/courses/hyperparameter-tuning-in-python)
- [ ] [Google: Testing and Debugging](https://developers.google.com/machine-learning/testing-debugging)
- [ ] [Troubleshooting Deep Neural Networks](http://josh-tobin.com/assets/pdf/troubleshooting-deep-neural-networks-01-19.pdf)
- [ ] [Youtube: How do GPUs speed up Neural Network training?](https://www.youtube.com/watch?v=EKD1kEMNeeU) `0:08:19`
- [ ] [Youtube: Why use GPU with Neural Networks?](https://www.youtube.com/watch?v=GRRMi7UfZHg) `0:09:43`
- [ ] [Youtube: Auto-Tuning Hyperparameters with Optuna and PyTorch](https://www.youtube.com/watch?v=P6NwZVl8ttc&t)

### Be familiar with literature on model interpretability
- [ ] [Article: How to leverage Explainable Machine Learning](https://www.maartengrootendorst.com/blog/black-boxes/)
- [ ] [Article: TracIn — A Simple Method to Estimate Training Data Influence](https://ai.googleblog.com/2021/02/tracin-simple-method-to-estimate.html)
- [ ] [NeurIPS 2020: Tutorial on Explaining ML Predictions: State-of-the-art, Challenges, and Opportunities](https://www.youtube.com/watch?v=EbpU4p_0hes&feature=emb_logo)
- [ ] [Youtube: Jay Alammar - Take A Look Inside Language Models With Ecco](https://www.youtube.com/watch?v=rHrItfNeuh0)

### Be able to optimize model size for production
- [ ] [Article: A Survey of Methods for Model Compression in NLP](https://www.pragmatic.ml/a-survey-of-methods-for-model-compression-in-nlp/)
- [ ] [Article: Why you should convert your NLP pipelines to ONNX](https://towardsdatascience.com/why-you-should-convert-your-nlp-pipelines-to-onnx-2c4a3433325f)
- [ ] [Article: Neural Network Pruning](https://nathanhubens.github.io/posts/deep%20learning/2020/05/22/pruning.html)
- [ ] [Article: FasterAI](https://nathanhubens.github.io/posts/deep%20learning/2020/08/17/FasterAI.html)
- [ ] [Article: Is the future of Neural Networks Sparse? An Introduction (1/N)](https://medium.com/huggingface/is-the-future-of-neural-networks-sparse-an-introduction-1-n-d03923ecbd70)
- [ ] [Article: Sparse Neural Networks (2/N): Understanding GPU Performance.](https://medium.com/huggingface/sparse-neural-networks-2-n-gpu-performance-b8bc9ce950fc)
- [ ] [Article: Block Sparse Matrices for Smaller and Faster Language Models](https://huggingface.co/blog/pytorch_block_sparse)
- [ ] [Article: Plunging Into Model Pruning in Deep Learning](https://wandb.ai/authors/pruning/reports/Plunging-Into-Model-Pruning-in-Deep-Learning--VmlldzoxMzcyMDg)
- [ ] [Article: How to accelerate and compress neural networks with quantization](https://towardsdatascience.com/how-to-accelerate-and-compress-neural-networks-with-quantization-edfbbabb6af7)

### Be able to write unit tests
- [ ] [Article: Effective testing for machine learning systems](https://www.jeremyjordan.me/testing-ml)
- [ ] [Article: Unit Testing for Data Scientists](https://www.maartengrootendorst.com/blog/unit/)
- [ ] [Article: Testing in Production, the safe way](https://copyconstruct.medium.com/testing-in-production-the-safe-way-18ca102d0ef1)
- [ ] [Article: How to cheat at unit tests with pytest and Black](https://simonwillison.net/2020/Feb/11/cheating-at-unit-tests-pytest-black/)
- [ ] [Article: 4 Lesser-Known Yet Awesome Tips for Pytest](https://towardsdatascience.com/4-lessor-known-yet-awesome-tips-for-pytest-2117d8a62d9c)
- [ ] [Datacamp: Unit Testing for Data Science in Python](https://www.datacamp.com/courses/unit-testing-for-data-science-in-python)
- [ ] [Pluralsight: Test-driven Development: The Big Picture](https://www.pluralsight.com/courses/test-driven-development-big-picture)
- [ ] [Test Driven Development with Python](http://chimera.labs.oreilly.com/books/1234000000754/index.html)
- [ ] [Thoughtbot: Fundamentals of TDD](https://thoughtbot.com/upcase/fundamentals-of-tdd)
- [ ] [Udacity: Software Analysis & Testing](https://www.udacity.com/course/software-analysis-testing--ud333)
- [ ] [Udacity: Software Testing](https://www.udacity.com/course/software-testing--cs258)
- [ ] [Udacity: Software Debugging](https://www.udacity.com/course/software-debugging--cs259)
- [ ] [Youtube: Beyond Accuracy: Behavioral Testing of NLP Models with CheckList | AISC](https://www.youtube.com/watch?v=A0od6RosVSA)

### Be able to serve models via APIs
- [ ] [Article: Deploy a Keras Deep Learning Project to Production with Flask](https://www.curiousily.com/posts/deploy-keras-deep-learning-project-to-production-with-flask/)
- [ ] [Article: Deploy BERT for Sentiment Analysis as REST API using PyTorch, Transformers by Hugging Face and FastAPI](https://www.curiousily.com/posts/deploy-bert-for-sentiment-analysis-as-rest-api-using-pytorch-transformers-by-hugging-face-and-fastapi/)
- [ ] [Article: Microservice in Python using FastAPI](https://dev.to/paurakhsharma/microservice-in-python-using-fastapi-24cc)
- [ ] [Article: Selecting gunicorn worker types for different python web applications.](https://medium.com/@nhudinhtuan/gunicorn-worker-types-practice-advice-for-better-performance-7a299bb8f929)
- [ ] [Article: Better performance by optimizing Gunicorn config](https://medium.com/building-the-system/gunicorn-3-means-of-concurrency-efbb547674b7)
- [ ] [Django Best Practices](http://slides.com/sudipkafle/django-best-practices)
- [ ] [Udacity: Authentication & Authorization: OAuth](https://www.udacity.com/course/authentication-authorization-oauth--ud330)
- [ ] [Udacity: HTTP & Web Servers](https://www.udacity.com/course/http-web-servers--ud303)
- [ ] [Udacity: Designing RESTful APIs](https://www.udacity.com/course/designing-restful-apis--ud388)
- [ ] [Udacity: Client-Server Communication](https://www.udacity.com/course/client-server-communication--ud897)
- [ ] [Youtube: PyConBY 2020: Sebastian Ramirez - Serve ML models easily with FastAPI](https://www.youtube.com/watch?v=z9K5pwb0rt8)
- [ ] [Youtube: FastAPI from the ground up](https://www.youtube.com/watch?v=3DLwPcrE5mA)
- [ ] [Youtube: Python pydantic Introduction – Give your data classes super powers](https://www.youtube.com/watch?v=WJmqgJn9TXg)

### Be able to build interactive UI for models
- [ ] [Article: Build and Deploy a Dashboard with Streamlit](https://www.maartengrootendorst.com/blog/streamlit/)
- [ ] [Article: New layout options for Streamlit](https://blog.streamlit.io/introducing-new-layout-options-for-streamlit/)

### Be able to deploy model to production
- [ ] [Acloudguru: AWS Certified Machine Learning - Specialty](https://acloud.guru/learn/aws-certified-machine-learning-specialty)
- [ ] [Acloudguru: AWS Certified Developer - Associate](https://acloud.guru/learn/aws-certified-developer-associate-june-2018)
- [ ] [Acloudguru: AWS Certification Preparation Guide](https://acloud.guru/learn/aws-certification-preparation)
- [ ] [AWS: Exam Readiness: AWS Certified Developer – Associate](https://www.aws.training/training/schedule?courseId=18953)
- [ ] [AWS: Thirty Serverless Architectures in 30 Minutes](https://www.youtube.com/watch?v=xJcm9V2jagc)
- [ ] [Article: Celery Execution Pools: What is it all about?](https://www.distributedpython.com/2018/10/26/celery-execution-pool/)
- [ ] [Article: Getting machine learning to production](http://veekaybee.github.io/2020/06/09/ml-in-prod/)
- [ ] [Article: A Guide to Production Level Deep Learning](https://github.com/alirezadir/Production-Level-Deep-Learning)
- [ ] [Article: MLOps concepts for busy engineers: model serving](https://spell.ml/blog/mlops-concepts-model-serving-X385lREAACcAAGzS)
- [ ] [Article: How to put machine learning models into production](https://stackoverflow.blog/2020/10/12/how-to-put-machine-learning-models-into-production/)
- [ ] [Article: Monitoring your Machine Learning Model](https://www.maartengrootendorst.com/blog/monitor/)
- [ ] [Article: How to Deploy a Machine Learning Model](https://www.maartengrootendorst.com/blog/deploy/)
- [ ] [Article: Smaller Docker images with Conda](https://jcristharif.com/conda-docker-tips.html)
- [ ] [Article: Deploying conda environments in (Docker) containers - how to do it right](https://uwekorn.com/2021/03/01/deploying-conda-environments-in-docker-how-to-do-it-right.html)
- [ ] [Article: How to scale services using Docker Compose](https://medium.com/@karthi.net/how-to-scale-services-using-docker-compose-31d7b83a6648)
- [ ] [Article: Understand Linux Load Averages and Monitor Performance of Linux](https://www.tecmint.com/understand-linux-load-averages-and-monitor-performance/)
- [ ] [Article: Preventing model drift with continuous monitoring and deployment using Github Actions and Algorithmia Insights](https://algorithmia.com/blog/preventing-model-drift-with-continuous-monitoring-and-deployment-using-github-actions-and-algorithmia-insights)
- [ ] [Article: Building a feature store](http://nlathia.github.io/2020/12/Building-a-feature-store.html)
- [ ] [Article: Combining rule engines and machine learning](http://nlathia.github.io/2020/10/ML-and-rule-engines.html)
- [ ] [Article: Model artifacts: the war stories](http://nlathia.github.io/2020/09/Model-artifacts-war-stories.html)
- [ ] [Article: Shadow mode deployments](http://nlathia.github.io/2020/07/Shadow-mode-deployments.html)
- [ ] [Article: When is a neural net too big for production?](http://nlathia.github.io/2019/09/Large-NLP-in-prod.html)
- [ ] [Article: Tensorflow in Docker](http://blog.tabanpour.info/projects/2018/09/07/tf-docker-kube.html)
- [ ] [Article: How to build scalable Machine Learning systems — Part 1/2](https://towardsdatascience.com/being-a-data-scientist-does-not-make-you-a-software-engineer-c64081526372)
- [ ] [Article: Architecting a Machine Learning Pipeline](https://towardsdatascience.com/architecting-a-machine-learning-pipeline-a847f094d1c7)
- [ ] [Article: Deploying Machine Learning Models: A Checklist](https://twolodzko.github.io/ml-checklist)
- [ ] [Article: How to Serve Models](http://bugra.github.io/posts/2020/5/25/how-to-serve-model/)
- [ ] [Article: How to Monitor Models](http://bugra.github.io/posts/2020/11/24/how-to-monitor-models/)
- [ ] [Article: Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance](https://towardsdatascience.com/production-machine-learning-monitoring-outliers-drift-explainers-statistical-performance-d9b1d02ac158)
- [ ] [Article: Machine learning is going real-time](https://huyenchip.com/2020/12/27/real-time-machine-learning.html)
- [ ] [Article: The correct way to evaluate online machine learning models](https://maxhalford.github.io/blog/online-learning-evaluation/)
- [ ] [Article: Machine Learning to Production](https://vikramriyer.github.io/machine-learning-to-production/#)
- [ ] [Article: Online batching with Spell serving](https://spell.ml/blog/online-batching-with-spell-serving-X_OKiBAAAASq6cL0)
- [ ] [Article: A deep dive into AWS spot instance interruptions](https://spell.ml/blog/aws-spot-interrupts-X_ZJ6xAAACEA9fGx)
- [ ] [Article: Key Concepts for Deploying Machine Learning Models to Mobile](https://spell.ml/blog/key-concepts-for-deploying-machine-learning-models-X7Q0phIAACIAqMWr)
- [ ] [Article: Configuring Gunicorn for Docker](https://pythonspeed.com/articles/gunicorn-in-docker/)
- [ ] [Article: How To Pass Environment Info During Docker Builds](https://blog.bitsrc.io/how-to-pass-environment-info-during-docker-builds-1f7c5566dd0e)
- [ ] [Article: Pass Docker Environment Variables During The Image Build](https://vsupalov.com/docker-build-pass-environment-variables/)
- [ ] [Article: Distill: Why do we need Flask, Celery, and Redis? (with McDonalds in Between)](https://ljvmiranda921.github.io/notebook/2019/11/08/flask-redis-celery-mcdo/)
- [ ] [Article: ML Infrastructure Tools for Data Preparation](https://towardsdatascience.com/ml-infrastructure-tools-for-data-preparation-70d79e70f8d4)
- [ ] [Article: ML Infrastructure Tools for Model Building](https://towardsdatascience.com/ml-infrastructure-tools-for-model-building-464770ac4fec)
- [ ] [Article: ML Infrastructure Tools for Production (Part 1)](https://towardsdatascience.com/ml-infrastructure-tools-for-production-1b1871eecafb)
- [ ] [Article: ML Infrastructure Tools for Production](https://towardsdatascience.com/ml-infrastructure-tools-for-production-part-2-model-deployment-and-serving-fcfc75c4a362)
- [ ] [Article: Using Statistical Distances for Machine Learning Observability](https://towardsdatascience.com/using-statistical-distance-metrics-for-machine-learning-observability-4c874cded78)
- [ ] [Article: ML Infrastructure Tools — ML Observability](https://towardsdatascience.com/ml-infrastructure-tools-ml-observability-8e4d7df6db43)
- [ ] [Article: The Model’s Shipped; What Could Possibly go Wrong?](https://towardsdatascience.com/the-models-shipped-what-could-possibly-go-wrong-a657ad919868)
- [ ] [Article: The Playbook to Monitor Your Model’s Performance in Production](https://towardsdatascience.com/the-playbook-to-monitor-your-models-performance-in-production-ec06c1cc3245)
- [ ] [Article: Setting Default Docker Environment Variables During Image Build](https://vsupalov.com/docker-build-time-env-values/)
- [ ] [Article: Getting oriented in the RAPIDS distributed ML ecosystem, part 1: ETL](https://spell.ml/blog/getting-oriented-in-the-rapids-distributed-ml-ecosystem-part-1-X1gixBIAAJ7nyzHa)
- [ ] [Article: Getting oriented in the RAPIDS distributed ML ecosystem, part 2: training and scoring](https://spell.ml/blog/getting-oriented-in-the-rapids-distributed-ml-ecosystem-part-2-X68AOxIAACAAacZk)
- [ ] [Article: Making model training scripts robust to spot interruptions](https://spell.ml/blog/spot-interruptions-XzQ5jRIAACIAK3h2)
- [ ] [Article: Getting started with large-scale ETL jobs using Dask and AWS EMR](https://spell.ml/blog/large-scale-etl-jobs-using-dask-Xyl8GhEAACQAjK6h)
- [ ] [Article: Distributed model training using Horovod](https://spell.ml/blog/distributed-model-training-using-horovod-XvqEGRUAACgAa5th)
- [ ] [Article: MLOps concepts for busy engineers: model serving](https://spell.ml/blog/mlops-concepts-model-serving-X385lREAACcAAGzS)
- [ ] [Article: Enough Docker to be Dangerous](http://seankross.com/2017/09/17/Enough-Docker-to-be-Dangerous.html)
- [ ] [Article: How Docker Can Help You Become A More Effective Data Scientist](https://towardsdatascience.com/how-docker-can-help-you-become-a-more-effective-data-scientist-7fc048ef91d5)
- [ ] [Article: How to properly ship and deploy your machine learning model](https://towardsdatascience.com/how-to-properly-ship-and-deploy-your-machine-learning-model-8a8664b763c4)
- [ ] [Article: Docker Explained Visually, For Non-Technical Folks](https://www.iteachrecruiters.com/blog/docker-explained-visually-for-non-technical-folks/)
- [ ] [Article: A Beginner-Friendly Introduction to Containers, VMs and Docker](https://www.freecodecamp.org/news/a-beginner-friendly-introduction-to-containers-vms-and-docker-79a9e3e119b/)
- [ ] [Article: The Playbook to Monitor Your Model’s Performance in Production](https://towardsdatascience.com/the-playbook-to-monitor-your-models-performance-in-production-ec06c1cc3245)
- [ ] [Blog: Python Speed](https://pythonspeed.com/)
	- [ ] [Connection refused? Docker networking and how it impacts your image](https://pythonspeed.com/articles/docker-connection-refused/)
	- [ ] [Faster or slower: the basics of Docker build caching](https://pythonspeed.com/articles/docker-caching-model/)
	- [ ] [Where’s your code? Debugging ImportError and ModuleNotFoundErrors in your Docker image](https://pythonspeed.com/articles/importerror-docker/)
	- [ ] [A tableau of crimes and misfortunes: the ever-useful docker history](https://pythonspeed.com/articles/docker-history/)
	- [ ] [Broken by default: why you should avoid most Dockerfile examples](https://pythonspeed.com/articles/dockerizing-python-is-hard/)
	- [ ] [A review of the official Dockerfile best practices: good, bad, and insecure](https://pythonspeed.com/articles/official-docker-best-practices/)
	- [ ] [The best Docker base image for your Python application (February 2021)](https://pythonspeed.com/articles/base-image-python-docker-images/)
	- [ ] [A deep dive into the official Docker image for Python](https://pythonspeed.com/articles/official-python-docker-image/)
	- [ ] [Using Alpine can make Python Docker builds 50× slower](https://pythonspeed.com/articles/alpine-docker-python/)
	- [ ] [When to switch to Python 3.9](https://pythonspeed.com/articles/switch-python-3.9/)
	- [ ] [Building on solid ground: ensuring reproducible Docker builds for Python](https://pythonspeed.com/articles/reproducible-docker-builds-python/)
	- [ ] [Push and pull: when and why to update your dependencies](https://pythonspeed.com/articles/when-update-dependencies/)
	- [ ] [Installing system packages in Docker with minimal bloat](https://pythonspeed.com/articles/system-packages-docker/)
	- [ ] [Less capabilities, more security: minimizing privilege escalation in Docker](https://pythonspeed.com/articles/root-capabilities-docker-security/)
	- [ ] [Avoiding insecure images from Docker build caching](https://pythonspeed.com/articles/docker-cache-insecure-images/)
	- [ ] [Build secrets in Docker and Compose, the secure way](https://pythonspeed.com/articles/build-secrets-docker-compose/)
	- [ ] [Security scanners for Python and Docker: from code to dependencies](https://pythonspeed.com/articles/docker-python-security-scan/)
	- [ ] [The high cost of slow Docker builds](https://pythonspeed.com/articles/high-cost-slow-docker-builds/)
	- [ ] [Faster Docker builds with pipenv, poetry, or pip-tools](https://pythonspeed.com/articles/pipenv-docker/)
	- [ ] [Elegantly activating a virtualenv in a Dockerfile](https://pythonspeed.com/articles/activate-virtualenv-dockerfile/)
	- [ ] [Poetry vs. Docker caching: Fight!](https://pythonspeed.com/articles/poetry-vs-docker-caching/)
	- [ ] [Speed up pip downloads in Docker with BuildKit’s new caching](https://pythonspeed.com/articles/docker-cache-pip-downloads/)
	- [ ] [Multi-stage builds #1: Smaller images for compiled code](https://pythonspeed.com/articles/smaller-python-docker-images/)
	- [ ] [Multi-stage builds #2: Python specifics—virtualenv, –user, and other methods](https://pythonspeed.com/articles/multi-stage-docker-python/)
	- [ ] [Multi-stage builds #3: Why your build is surprisingly slow, and how to speed it up](https://pythonspeed.com/articles/faster-multi-stage-builds/)
	- [ ] [Configuring Gunicorn for Docker](https://pythonspeed.com/articles/gunicorn-in-docker/)
	- [ ] [Activating a Conda environment in your Dockerfile](https://pythonspeed.com/articles/activate-conda-dockerfile/)
	- [ ] [Shrink your Conda Docker images with conda-pack](https://pythonspeed.com/articles/conda-docker-image-size/)
	- [ ] [Reproducible and upgradable Conda environments: dependency management with conda-lock](https://pythonspeed.com/articles/conda-dependency-management/)
	- [ ] [What’s running in production? Making your Docker images identifiable](https://pythonspeed.com/articles/identifying-images/)
	- [ ] [Decoupling database migrations from server startup: why and how](https://pythonspeed.com/articles/schema-migrations-server-startup/)
	- [ ] [A Python prompt into a running process: debugging with Manhole](https://pythonspeed.com/articles/live-debugging-python/)
	- [ ] [A thousand little details: developing software for ops](https://pythonspeed.com/articles/developing-tools-for-ops/)
	- [ ] [Your Docker build needs a smoke test](https://pythonspeed.com/articles/test-your-docker-build/)
	- [ ] [Where’s that log file? Debugging failed Docker builds](https://pythonspeed.com/articles/debugging-docker-build/)
	- [ ] [“Let’s use Kubernetes!” Now you have 8 problems](https://pythonspeed.com/articles/dont-need-kubernetes/)
	- [ ] [Docker BuildKit: faster builds, new features, and now it’s stable](https://pythonspeed.com/articles/docker-buildkit/)
	- [ ] [Options for packaging your Python code: Wheels, Conda, Docker, and more](https://pythonspeed.com/articles/distributing-software/)
	- [ ] [Docker vs. Singularity for data processing: UIDs and filesystem access](https://pythonspeed.com/articles/containers-filesystem-data-processing/)
- [ ] [Cortex Blog](https://www.cortex.dev/learn)
	- [ ] [Server-side batching: Scaling inference throughput in machine learning](https://www.cortex.dev/post/server-side-batching-scaling-inference-throughput)
	- [ ] [How we served 1,000 models on GPUs for $0.47](https://www.cortex.dev/post/how-we-served-1-000-models-on-gpus)
	- [ ] [Designing a machine learning platform for both data scientists and engineers](https://www.cortex.dev/post/designing-a-machine-learning-platform-for-both-data-scientists-and-engineers)
	- [ ] [How to serve batch predictions with TensorFlow Serving](https://www.cortex.dev/post/batch-prediction-with-tensorflow-serving)
	- [ ] [How to deploy Transformer models for language tasks](https://www.cortex.dev/post/how-to-deploy-text-transformer-models)
	- [ ] [How we scale machine learning model deployment on Kubernetes](https://www.cortex.dev/post/scale-machine-learning-model-deployment-kubernetes)
	- [ ] [Why we built a serverless machine learning platform—instead of using AWS Lambda](https://www.cortex.dev/post/serverless-machine-learning-aws-lambda)
	- [ ] [Why we don’t deploy machine learning models with Flask](https://www.cortex.dev/post/dont-deploy-machine-learning-models-with-flask)
	- [ ] [How to deploy machine learning models from a notebook to production](https://www.cortex.dev/post/deploy-machine-learning-models-from-a-notebook-to-production)
	- [ ] [A/B testing machine learning models in production](https://www.cortex.dev/post/a-b-testing-machine-learning-models)
	- [ ] [How to deploy 1,000 models on one CPU with TensorFlow Serving](https://www.cortex.dev/post/deploy-tensorflow-serving-multi-model-caching)
	- [ ] [How to reduce the cost of machine learning inference](https://www.cortex.dev/post/reduce-machine-learning-inference-cost)
	- [ ] [Improve NLP inference throughput 40x with ONNX and Hugging Face](https://www.cortex.dev/post/40x-nlp-inference-with-hugging-face-and-onnx)
	- [ ] [How to deploy PyTorch Lightning models to production](https://www.cortex.dev/post/deploy-pytorch-lightning-models)
- [ ] [Doc: Environment variables in Compose](https://docs.docker.com/compose/environment-variables/)
- [ ] [Doc: Lecture 3: Data engineering](https://docs.google.com/document/d/1b9iuZiDEGVLHyMmnf6w2y1aN6yWQhAyqk3GHlpI9q6M/edit)
- [ ] [Luigi Patruno: ML in Production](https://mlinproduction.com/)
    - [ ] [Video: You trained a machine learning model. Now what?](https://www.youtube.com/watch?v=Vugbn17LDPQ)
    - [ ] [Article: Docker for Machine Learning – Part I](https://mlinproduction.com/docker-for-ml-part-1/)
    - [ ] [Article: Docker for Machine Learning – Part II](https://mlinproduction.com/docker-for-ml-part-2/)
    - [ ] [Article: Docker for Machine Learning – Part III](https://mlinproduction.com/docker-for-ml-part-3/)
    - [ ] [Article: Using Docker to Generate Machine Learning Predictions in Real Time](https://mlinproduction.com/docker-for-ml-part-4/)
    - [ ] [Article: Batch Inference vs Online Inference](https://mlinproduction.com/batch-inference-vs-online-inference/)
    - [ ] [Article: Storing Metadata from Machine Learning Experiments](https://mlinproduction.com/ml-metadata/)
    - [ ] [Article: How Data Leakage Impacts Machine Learning Models](https://mlinproduction.com/data-leakage/)
    - [ ] [Article: An Introduction to Kubernetes for Data Scientists](https://mlinproduction.com/intro-to-kubernetes/)
    - [ ] [Article: How to Use Kubernetes Pods for Machine Learning](https://mlinproduction.com/k8s-pods/)
    - [ ] [Article: Kubernetes Jobs for Machine Learning](https://mlinproduction.com/k8s-jobs/)
    - [ ] [Article: Kubernetes CronJobs for Machine Learning](https://mlinproduction.com/k8s-cronjobs/)
    - [ ] [Article: Kubernetes Deployments for Machine Learning](https://mlinproduction.com/k8s-deployments/)
    - [ ] [Article: Kubernetes Services for Machine Learning](https://mlinproduction.com/k8s-services/)
    - [ ] [Article: The Ultimate Guide to Model Retraining](https://mlinproduction.com/model-retraining/)
    - [ ] [Article: Top ML Resources: Interview with Eric Colson](https://mlinproduction.com/top-30-ml-in-production-resources-guide-eric-colson-interview/)
    - [ ] [Article: Top ML Resources: Interview with Veronika Megler, PhD](https://mlinproduction.com/top-30-ml-in-production-resources-guide-veronika-megler-interview/)
    - [ ] [Article: Top ML Resources: Interview with Erik Bernhardsson](https://mlinproduction.com/top-30-ml-in-production-resources-guide-erik-bernhardsson-interview/)
    - [ ] [Article: Top ML Resources: Interview with Rui Carmo](https://mlinproduction.com/top-30-ml-in-production-resources-guide-rui-carmo-interview/)
    - [ ] [Article: Top ML Resources: Interview with Jeremy Jordan](https://mlinproduction.com/top-30-ml-in-production-resources-guide-jeremy-jordan-interview/)
    - [ ] [Article: 5 Challenges to Running Machine Learning Systems in Production](https://mlinproduction.com/5-challenges-to-ml-in-production-solve-them-with-aws-sagemaker/)
    - [ ] [Article: Enabling Machine-Learning-as-a-Service Through Privacy Preserving Machine Learning](https://mlinproduction.com/enabling-machine-learning-as-service-through-privacy-preserving-ml/)
    - [ ] [Article: What Does it Mean to Deploy a Machine Learning Model? (Deployment Series: Guide 01)](https://mlinproduction.com/what-does-it-mean-to-deploy-a-machine-learning-model-deployment-series-01/)
    - [ ] [Article: Software Interfaces for Machine Learning Deployment (Deployment Series: Guide 02)](https://mlinproduction.com/software-interfaces-for-machine-learning-deployment-deployment-series-02/)
    - [ ] [Article: Batch Inference for Machine Learning Deployment (Deployment Series: Guide 03)](https://mlinproduction.com/batch-inference-for-machine-learning-deployment-deployment-series-03/)
    - [ ] [Article: The Challenges of Online Inference (Deployment Series: Guide 04)](https://mlinproduction.com/the-challenges-of-online-inference-deployment-series-04/)
    - [ ] [Article: Online Inference for ML Deployment (Deployment Series: Guide 05)](https://mlinproduction.com/online-inference-for-ml-deployment-deployment-series-05/)
    - [ ] [Article: Model Registries for ML Deployment (Deployment Series: Guide 06)](https://mlinproduction.com/model-registries-for-ml-deployment-deployment-series-06/)
    - [ ] [Article: Test-Driven Machine Learning Development (Deployment Series: Guide 07)](https://mlinproduction.com/testing-machine-learning-models-deployment-series-07/)
    - [ ] [Article: A/B Testing Machine Learning Models (Deployment Series: Guide 08)](https://mlinproduction.com/ab-test-ml-models-deployment-series-08/)
    - [ ] [Article: Lessons Learned from 15 Years of Monitoring Machine Learning in Production](https://mlinproduction.com/lessons-learned-from-15-years-of-monitoring-machine-learning-in-production/)
    - [ ] [Article: Why is it Important to Monitor Machine Learning Models?](https://mlinproduction.com/why-is-it-important-to-monitor-machine-learning-models/)
    - [ ] [Article: Maximizing Business Impact with Machine Learning](https://mlinproduction.com/maximizing-business-impact-with-machine-learning/)
- [ ] [Article: Proxy Metrics](https://gibsonbiddle.medium.com/4-proxy-metrics-a82dd30ca810)
- [ ] [Article: Celery: an overview of the architecture and how it works](https://www.vinta.com.br/blog/2017/celery-overview-archtecture-and-how-it-works/)
- [ ] [Article: Unit Testing Celery Tasks](https://www.distributedpython.com/2018/05/01/unit-testing-celery-tasks/)
- [ ] [Article: Testing Celery Chains](https://www.distributedpython.com/2018/05/15/testing-celery-chains/)
- [ ] [Article: Task Routing in Celery](https://www.distributedpython.com/2018/05/29/task-routing/)
- [ ] [Article: Dynamic Task Routing in Celery](https://www.distributedpython.com/2018/06/05/dynamic-task-routing/)
- [ ] [Article: Dockerize a Celery app with Django and RabbitMQ](https://www.distributedpython.com/2018/06/12/celery-django-docker/)
- [ ] [Article: How to call a Celery task from another app](https://www.distributedpython.com/2018/06/19/call-celery-task-outside-codebase/)
- [ ] [Article: Distributed Monte Carlo with Celery chords](https://www.distributedpython.com/2018/06/26/celery-monte-carlo/)
- [ ] [Article: An incredibly simple no-frills Celery setup](https://www.distributedpython.com/2018/07/03/simple-celery-setup/)
- [ ] [Article: 3 Strategies to Customise Celery logging handlers](https://www.distributedpython.com/2018/08/28/celery-logging/)
- [ ] [Article: Celery task exceptions and automatic retries](https://www.distributedpython.com/2018/09/04/error-handling-retry/)
- [ ] [Article: Concurrency and Parallelism](https://www.distributedpython.com/2018/09/14/concurrency-and-parallelism/)
- [ ] [Article: Celery, docker and the missing startup banner](https://www.distributedpython.com/2018/10/01/celery-docker-startup/)
- [ ] [Article: Monitoring a Dockerized Celery Cluster with Flower](https://www.distributedpython.com/2018/10/13/flower-docker/)
- [ ] [Article: Quick Guide: Custom Celery Task Logger](https://www.distributedpython.com/2018/11/06/celery-task-logger-format/)
- [ ] [Article: Celery on Docker: From the Ground up](https://www.distributedpython.com/2018/11/15/celery-docker/)
- [ ] [Article: Kubernetes for Python Developers: Part 1](https://www.distributedpython.com/2018/11/28/kubernetes-python-developers-part-1/)
- [ ] [Article: Auto-reload Celery on code changes](https://www.distributedpython.com/2019/04/23/celery-reload/)
- [ ] [Book: Building Intelligent Systems: A Guide to Machine Learning Engineering](https://www.amazon.com/Building-Intelligent-Systems-Learning-Engineering/dp/1484234316)
- [ ] [Datacamp: Parallel Computing with Dask](https://www.datacamp.com/courses/parallel-computing-with-dask)
- [ ] [Datacamp: Data Engineering for Everyone](https://www.datacamp.com/courses/data-engineering-for-everyone)
- [ ] [Datacamp: Cloud Computing for Everyone](https://www.datacamp.com/courses/cloud-computing-for-everyone)
- [ ] [Pluralsight: Docker and Containers: The Big Picture](https://www.pluralsight.com/courses/docker-containers-big-picture)
- [ ] [Pluralsight: Docker and Kubernetes: The Big Picture](https://www.pluralsight.com/courses/docker-kubernetes-big-picture)
- [ ] [Pluralsight: AWS Developer: The Big Picture](https://www.pluralsight.com/courses/aws-developer-big-picture)
- [ ] [Pluralsight: AWS Networking Deep Dive: Virtual Private Cloud (VPC)](https://www.pluralsight.com/courses/aws-networking-deep-dive-vpc)
- [ ] [Pluralsight: AWS VPC Operations](https://www.pluralsight.com/courses/aws-vpc-operations)
- [ ] [Pluralsight: Building Applications Using Elastic Beanstalk](https://www.pluralsight.com/courses/elastic-beanstalk-building-applications)
- [ ] [Servers for Hackers Series](https://serversforhackers.com/)
- [ ] [The Hacker's Guide to Scaling Python](https://scaling-python.com/)
- [ ] [Udacity: Intro to DevOps](https://www.udacity.com/course/intro-to-devops--ud611)
- [ ] [Udacity: Configuring Linux Web Servers](https://www.udacity.com/course/configuring-linux-web-servers--ud299)
- [ ] [Udacity: Scalable Microservices with Kubernetes](https://www.udacity.com/course/scalable-microservices-with-kubernetes--ud615)
- [ ] [Udemy: AWS Concepts](https://www.udemy.com/aws-concepts)
- [ ] [Udemy: Serverless Concepts](https://www.udemy.com/serverless-concepts/)
- [ ] [Udemy: AWS Certified Developer - Associate 2018](https://www.udemy.com/aws-certified-developer-associate/)
- [ ] [Whitepaper: Architecting for the Cloud AWS Best Practices](https://d1.awsstatic.com/whitepapers/AWS_Cloud_Best_Practices.pdf)
- [ ] [Whitepaper: AWS Well-Architected Framework](https://d1.awsstatic.com/whitepapers/architecture/AWS_Well-Architected_Framework.pdf)
- [ ] [Whitepaper: AWS Security Best Practices](https://d1.awsstatic.com/whitepapers/Security/AWS_Security_Best_Practices.pdf)
- [ ] [Whitepaper: Blue/Green Deployments on AWS](https://d1.awsstatic.com/whitepapers/AWS_Blue_Green_Deployments.pdf)
- [ ] [Whitepaper: Microservices on AWS](https://docs.aws.amazon.com/aws-technical-content/latest/microservices-on-aws/microservices-on-aws.pdf)
- [ ] [Whitepaper: Optimizing Enterprise Economics with Serverless Architectures](https://d1.awsstatic.com/whitepapers/optimizing-enterprise-economics-serverless-architectures.pdf)
- [ ] [Whitepaper: Practicing Continuous Integration and Continuous Delivery on AWS](https://d1.awsstatic.com/whitepapers/DevOps/practicing-continuous-integration-continuous-delivery-on-AWS.pdf)
- [ ] [Whitepaper: Running Containerized Microservices on AWS](https://d1.awsstatic.com/whitepapers/DevOps/running-containerized-microservices-on-aws.pdf)
- [ ] [Whitepaper: Serverless Architectures with AWS Lambda](https://d1.awsstatic.com/whitepapers/serverless-architectures-with-aws-lambda.pdf)
- [ ] [Youtube: Deploying a machine learning model to the cloud using AWS Lambda](https://www.youtube.com/watch?v=4ocbx9IeBMU&feature=youtu.be)
- [ ] [Youtube: Shawn Scully: Production and Beyond: Deploying and Managing Machine Learning Models](https://www.youtube.com/watch?v=q-VPALG6ogY&feature=youtu.be)
- [ ] [Youtube: Human-Centric Machine Learning Infrastructure @Netflix](https://www.youtube.com/watch?v=XV5VGddmP24&feature=youtu.be)
- [ ] [Youtube: Instrumentation, Observability & Monitoring of Machine Learning Models](https://www.infoq.com/presentations/instrumentation-observability-monitoring-ml/)
- [ ] [Youtube: OpML '20 - How ML Breaks: A Decade of Outages for One Large ML Pipeline](https://www.youtube.com/watch?v=hBMHohkRgAA)
- [ ] [Youtube: Applied ML in Production](https://www.youtube.com/playlist?list=PLqy_sIcckLC2jrxQhyqWDhL_9Uwxz8UFq)
    - [ ] [Objective · Applied ML in Production](https://www.youtube.com/watch?v=_sYrVHGRqPo) `0:02:38`
    - [ ] [Solution · Applied ML in Production](https://www.youtube.com/watch?v=Gi1VlFV8e_k) `0:08:53`
    - [ ] [Evaluation · Applied ML in Production](https://www.youtube.com/watch?v=AwajdDVR_C4) `0:04:02`
    - [ ] [Iteration · Applied ML in Production](https://www.youtube.com/watch?v=Bit1IUVWrkY) `0:04:35`
    - [ ] [Annotation · Applied ML in Production](https://www.youtube.com/watch?v=Kj_5ZO6nsfk) `0:14:34`
    - [ ] [Exploratory Data Analysis (EDA) · Applied ML in Production](https://www.youtube.com/watch?v=3Vrlvrjigvs&feature=emb_logo) `0:09:02`
- [ ] [MLOps Community Talks](https://www.youtube.com/playlist?list=PL3vkEKxWd-uutUO2zB0diYlANmsnbGizD)
	- [ ] [Our 1st MLOps Meetup - Luke Marsden](https://www.youtube.com/watch?v=P5cNwyeq0_c) `0:56:11`
	- [ ] [MLOps Community  Meetup #3: Hierarchy of Machine Learning Needs with Phil Winder](https://www.youtube.com/watch?v=MRES5IxVnME) `0:58:25`
	- [ ] [MLOps Community March 25 2020 featuring Charles Radclyffe](https://www.youtube.com/watch?v=l52sRMVPVk0) `1:03:42`
	- [ ] [MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey](https://www.youtube.com/watch?v=oq1g4s2dUHE) `0:55:42`
	- [ ] [MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali](https://www.youtube.com/watch?v=1CcYuVVwOGg) `1:01:35`
	- [ ] [MLOps meetup #5 High Stakes ML with Flavio CLesio](https://www.youtube.com/watch?v=9g4deV1uNZo) `0:55:27`
	- [ ] [MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline](https://www.youtube.com/watch?v=cUxK28ocZcw) `0:56:17`
	- [ ] [#8 Optimizing your ML workflow with kubeflow 1.0](https://www.youtube.com/watch?v=jXRbj5xnBy4) `1:03:41`
	- [ ] [#10 MLOps the Elephant and the Blind Men with Saurav Chakravorty](https://www.youtube.com/watch?v=RTBq7e3FhEw) `0:55:02`
	- [ ] [#11 Machine Learning at scale in Mercado Libre with Carlos de la Torre](https://www.youtube.com/watch?v=ypySVdT9U7Q) `0:59:28`
	- [ ] [MLOps meetup #12 // Why data scientists should know data engineering with Dan Sullivan](https://www.youtube.com/watch?v=LdnmC6PDBuY) `0:58:28`
	- [ ] [#13 Maximizing job opportunities as a data scientists with Anthony Kelly](https://www.youtube.com/watch?v=-xAyfWL_HXs) `0:58:24`
	- [ ] [MLOps #14: Kubeflow vs MLflow with Byron Allen](https://www.youtube.com/watch?v=TsGQZ0D3688) `0:54:57`
	- [ ] [MLOps #15 - Scaling Human in the Loop Machine Learning with Robert Munro](https://www.youtube.com/watch?v=LwbbGsuNpao) `0:55:04`
	- [ ] [#16 Venture Capital In Machine Learning Startups with John Spindler](https://www.youtube.com/watch?v=v9OvXxTUBtg) `1:05:41`
	- [ ] [MLOps #17 // The challenges of ML Operations and how Hermione helps along the way](https://www.youtube.com/watch?v=cDRXLqKJ6I0) `1:01:30`
	- [ ] [MLOps #18 // Nubank - Running a fintech on ML](https://www.youtube.com/watch?v=ry_P5D_d7XA) `0:53:19`
	- [ ] [MLOps #19 // DataOps and Data versioning in ML](https://www.youtube.com/watch?v=ojV1tK9jXH8) `1:01:55`
	- [ ] [MLOps #21 Build vs Buy an ML platform // Diego Oppenheimer - CEO Algorithmia](https://www.youtube.com/watch?v=1bHQE11Qq0k) `0:57:20`
	- [ ] [MLOps #22 Deep Dive on Paperspace Tooling // Misha Kutsovsky - Senior ML Architect at Paperspace](https://www.youtube.com/watch?v=qMHUE5b1Ee4) `1:07:15`
	- [ ] [Feature Stores: An essential part of the ML stack to build great data / Kevin Stumpf - CTO at Tecton](https://www.youtube.com/watch?v=IjO8VUCIZxc) `1:05:46`
	- [ ] [MLOps #24 Monitoring the ML stack // Lina Weichbrodt](https://www.youtube.com/watch?v=Un30yb1WlpU) `0:55:32`
	- [ ] [MLOps #25 - How to become a better data scientist: the definite guide // Alexey Grigorev](https://www.youtube.com/watch?v=f531HULH8KA) `1:00:42`
	- [ ] [MLOps #26 Python and Dask: Scaling the DataFrame // Dan Gerlanc - Founder of Enplus Advisors](https://www.youtube.com/watch?v=wUokn3RQ9_A) `1:28:56`
	- [ ] [#27 How to Leverage ML Tooling Ecosystem  Mariya Davydova   Head of Product at Neu ro](https://www.youtube.com/watch?v=kZ4F48Er__k) `0:55:57`
	- [ ] [MLOps #28 ML Observability // Aparna Dhinakaran - Chief Product Officer at Arize AI](https://www.youtube.com/watch?v=joTF9BRwWp4) `0:55:04`
	- [ ] [MLOps #29 Continuous Evaluation & Model Experimentation // Danny Ma - Founder of Sydney Data Science](https://www.youtube.com/watch?v=i4QNpM20QOc) `1:00:46`
	- [ ] [MLOps Coffee Sessions #6 Continuous Integration for ML // Featuring Elle O'Brien](https://www.youtube.com/watch?v=L98VxJDHXMM) `1:01:46`
	- [ ] [MLOps #30 Scaling ML Capabilities in Large Organizations // Bertjan Broeksema & Axel Goblet](https://www.youtube.com/watch?v=aeYnfU26WGk) `1:02:47`
	- [ ] [MLOps #31 Path to Production and Monetizing Machine Learning // Vin Vashishta - Data Scientist](https://www.youtube.com/watch?v=voO0B0_BsuQ) `0:56:35`
	- [ ] [MLOps #32 Creating Beautiful Ambient Music with Google Brain’s Music Transformer // Daniel Jeffries](https://www.youtube.com/watch?v=z95ciIqMuRo) `0:55:52`
	- [ ] [MLOps #33 Building Say Less: An AI-Powered Summarization App // Yoav Zimmerman - Model Zoo](https://www.youtube.com/watch?v=RoYl0waMfMc) `0:53:08`
	- [ ] [MLOps #34 Owned By Statistics: How Kubeflow & MLOps Can Help Secure ML Workloads // David Aronchick](https://www.youtube.com/watch?v=ml4vlXzVFeE) `0:56:18`
	- [ ] [MLOps #35: Streaming Machine Learning with Apache Kafka and Tiered Storage // Kai Waehner, Confluent](https://www.youtube.com/watch?v=Ur969-WX1BY) `0:52:50`
	- [ ] [Bring Your On-Prem ML Use Cases to Production on Google Cloud using Kubeflow](https://www.youtube.com/watch?v=JZAc_yZgByg) `0:20:28`
	- [ ] [Moving deep learning from research to prod using DeterminedAI and Kubeflow // David Hershey](https://www.youtube.com/watch?v=-UymDRk5ISY) `0:56:06`
	- [ ] [MLOps Coffee Sessions #11: Analyzing “Continuous Delivery and Automation Pipelines in ML" // Part 3](https://www.youtube.com/watch?v=sL2Z_s0ZkPo) `1:06:28`
	- [ ] [MLOps Coffee Sessions #14 Conversation with the creators of Dask // Hugo Bowne and Matthew Rocklin](https://www.youtube.com/watch?v=tXckHqfkPqg) `0:56:27`
	- [ ] [When You Say Data Scientist Do You Mean Data Engineer? Lessons Learned From Start Up Life](https://www.youtube.com/watch?v=v2HzCcAT1t8) `1:00:43`
	- [ ] [Scalable Python for Everyone, Everywhere // Matthew Rocklin // MLOps  Meetup #37](https://www.youtube.com/watch?v=FIJuh4PXkrk) `0:57:07`
	- [ ] [Operationalize Open Source Models with SAS Open Model Manager // Ivan Nardini // MLOps Meetup #39](https://www.youtube.com/watch?v=SNRsTYmb19U) `0:56:53`
	- [ ] [Data Engineering + ML + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16](https://www.youtube.com/watch?v=IUhvO8QjWd8) `0:57:05`
	- [ ] [Hands-on serving models using KFserving // Theofilos Papapanagiotou // MLOps Meetup #40](https://www.youtube.com/watch?v=VtZ9LWyJPdc) `0:57:40`
	- [ ] [Luigi in Production // MLOps Coffee Sessions #18 // Luigi Patruno ML in Production](https://www.youtube.com/watch?v=ShBod1yXUeg) `0:47:23`
	- [ ] [Metaflow: Supercharging Our Data Scientist Productivity // Ravi Kiran Chirravuri // MLOps Meetup #41](https://www.youtube.com/watch?v=1iuJvF_SE9Q) `1:00:30`
	- [ ] [UN Global Platform // Mark Craddock // Co-Founder & CTO, Global Certification // MLOps Meetup #42](https://www.youtube.com/watch?v=6oFCqGE_2-U) `0:58:48`
	- [ ] [The Current MLOps Landscape // Nathan Benaich & Timothy Chen // MLOps Meetup #43](https://www.youtube.com/watch?v=i6HZ2vjFLIs) `0:58:31`
	- [ ] [A Conversation with Seattle Data Guy // Benjamin Rogojan // MLOps Coffee Sessions #21](https://www.youtube.com/watch?v=o-YXAq9vii4) `0:47:11`
- [ ] [Youtube: Josh Wills: Visibility and Monitoring for Machine Learning Models](https://youtu.be/zbS9jBB8fz8)
- [ ] [Youtube: PyData Vancouver meetup: cortex.dev : Serving machine learning models in production](https://www.youtube.com/watch?v=_2HMDhgPkSQ)
- [ ] [Youtube: Why Your Web Server Should Log to Stdout (Especially with Docker)](https://www.youtube.com/watch?v=aQikNWEaJUQ&feature=emb_logo)
- [ ] [Youtube: What is Load Balancing?](https://www.youtube.com/watch?v=K0Ta65OqQkY)
- [ ] [Youtube: What is Consistent Hashing and Where is it used?](https://www.youtube.com/watch?v=zaRkONvyGr8)
- [ ] [Youtube: What is a Message Queue and Where is it used?](https://www.youtube.com/watch?v=oUJbuFMyBDk)
- [ ] [Youtube: Why do Databases fail? AntiPatterns to avoid!](https://www.youtube.com/watch?v=9T-gNZ5bGCw)
- [ ] [Youtube: What is a microservice architecture and it's advantages?](https://www.youtube.com/watch?v=qYhRvH9tJKw)
- [ ] [Youtube: What is the Publisher Subscriber Model?](https://www.youtube.com/watch?v=FMhbR_kQeHw)
- [ ] [Youtube: What's an Event Driven System?](https://www.youtube.com/watch?v=rJHTK2TfZ1I)
- [ ] [Youtube: 5 Tips for System Design Interviews](https://www.youtube.com/watch?v=CtmBGH8MkX4)
- [ ] [Youtube: How to avoid a single point of failure in distributed systems](https://www.youtube.com/watch?v=-BOysyYErLY)
- [ ] [Youtube: System Design: Tinder as a microservice architecture](https://www.youtube.com/watch?v=tndzLznxq40)
- [ ] [Youtube: System Design Basics: Horizontal vs. Vertical Scaling](https://www.youtube.com/watch?v=xpDnVSmNFX0)
- [ ] [Youtube: What is Database Sharding?](https://www.youtube.com/watch?v=5faMjKuB9bc)
- [ ] [Youtube: A friendly introduction to System Design](https://www.youtube.com/watch?v=quLrc3PbuIw)
- [ ] [Youtube: Avoid cascading failures in a distributed system](https://www.youtube.com/watch?v=xrizarXJgC8)
- [ ] [Youtube: Designing Instagram: System Design of News Feed](https://www.youtube.com/watch?v=QmX2NPkJTKg)
- [ ] [Youtube: Whatsapp System Design: Chat Messaging Systems for Interviews](https://www.youtube.com/watch?v=vvhC64hQZMk)
- [ ] [Youtube: Introduction to NoSQL databases](https://www.youtube.com/watch?v=xQnIN9bW0og)
- [ ] [Youtube: Distributed Consensus and Data Replication strategies on the server](https://www.youtube.com/watch?v=GeGxgmPTe4c)
- [ ] [Youtube: What is an API and how do you design it?](https://www.youtube.com/watch?v=_YlYuNMTCc8)
- [ ] [Youtube: What is Distributed Caching? Explained with Redis!](https://www.youtube.com/watch?v=U3RkDLtS7uY)
- [ ] [Youtube: Service discovery and heartbeats in micro-services](https://www.youtube.com/watch?v=lWE_UIbm8NA)
- [ ] [Youtube: Relational database index vs. NoSQL index](https://www.youtube.com/watch?v=mTNkqMDCasI)
- [ ] [Youtube: How Netflix onboards new content: Video Processing at scale](https://www.youtube.com/watch?v=x9Hrn0oNmJM)
- [ ] [Youtube: How to start with distributed systems? Beginner's guide to scaling systems.](https://www.youtube.com/watch?v=SqcXvc3ZmRU)
- [ ] [Youtube: Capacity Estimation: How much data does YouTube store daily?](https://www.youtube.com/watch?v=0myM0k1mjZw)
- [ ] [Youtube: How databases scale writes: The power of the log](https://www.youtube.com/watch?v=_5vrfuwhvlQ)
- [ ] [Youtube: System design : Design Autocomplete or Typeahead Suggestions for Google search](https://www.youtube.com/watch?v=us0qySiUsGU)

### Be able to perform load testing
- [ ] [Youtube: Loading Testing with Python](https://www.youtube.com/playlist?list=PLotCx_Au_rT1LW_qpMWU40Q-vegZua-i8)

### Be able to perform A/B testing
- [ ] [Article: Multi-Armed Bandit (MAB) – A/B Testing Sans Regret](https://vwo.com/blog/multi-armed-bandit-algorithm/)
- [ ] [Article: When to Run Bandit Tests Instead of A/B/n Tests](https://cxl.com/blog/bandit-tests/)
- [ ] [Datacamp: Customer Analytics & A/B Testing in Python](https://www.datacamp.com/courses/customer-analytics-ab-testing-in-python)
- [ ] [Udacity: A/B Testing](https://www.udacity.com/course/ab-testing--ud257)
- [ ] [Udacity: A/B Testing for Business Analysts](https://www.udacity.com/course/ab-testing--ud979)
- [ ] [Youtube: Hypothesis testing with Applications in Data Science](https://www.youtube.com/watch?v=kx-pcQAPvoc) `0:10:33`

### Be proficient in Python
- [ ] [Article: No Really, Python's Pathlib is Great](https://rednafi.github.io/digressions/python/2020/04/13/python-pathlib.html)
- [ ] [Article: A deep dive on Python type hints](http://veekaybee.github.io/2019/07/08/python-type-hints/)
- [ ] [Article: I wish I knew these things when I learned Python](http://bugra.github.io/posts/2015/1/3/i-wish-i-knew-these-things-when-i-learned-python/)
- [ ] [Article: Python Concurrency: The Tricky Bits](https://python.hamel.dev/concurrency/)
- [ ] [Article: The Complete Python Development Guide](https://testdriven.io/guides/complete-python/)
- [ ] [Article: Hypermodern Python](https://cjolowicz.github.io/posts/hypermodern-python-01-setup/)
- [ ] [Article: Hypermodern Python Chapter 2: Testing](https://cjolowicz.github.io/posts/hypermodern-python-02-testing/)
- [ ] [Article: Hypermodern Python Chapter 3: Linting](https://cjolowicz.github.io/posts/hypermodern-python-03-linting/)
- [ ] [Article: Hypermodern Python Chapter 4: Typing](https://cjolowicz.github.io/posts/hypermodern-python-04-typing/)
- [ ] [Article: Hypermodern Python Chapter 5: Documentation](https://cjolowicz.github.io/posts/hypermodern-python-05-documentation/)
- [ ] [Article: Hypermodern Python Chapter 6: CI/CD](https://cjolowicz.github.io/posts/hypermodern-python-06-ci-cd/)
- [ ] [Article: Speeding Up Python with Concurrency, Parallelism, and asyncio](https://testdriven.io/blog/concurrency-parallelism-asyncio/)
- [ ] [Article: Speed Up Your Python Program With Concurrency](https://testdriven.io/blog/concurrency-parallelism-asyncio/)
- [ ] [Regex For Noobs (like me!) - An Illustrated Guide](https://www.janmeppe.com/blog/regex-for-noobs/)
- [ ] [Book: A Byte of Python](https://python.swaroopch.com)
- [ ] [Book: Learn Python The Hard way](https://learnpythonthehardway.org)
- [ ] [Book: Python 201](https://leanpub.com/python201)
- [ ] [Book: Python Anti-Patterns](https://docs.quantifiedcode.com/python-anti-patterns/index.html)
- [ ] [Book: Real Python](https://www.goodreads.com/book/show/20750754-real-python)
- [ ] [Book: The Python 3 Standard Library By Example](https://doughellmann.com/blog/the-python-3-standard-library-by-example)
- [ ] [Book: Writing Idiomatic Python 3](https://www.amazon.com/Writing-Idiomatic-Python-Jeff-Knupp-ebook/dp/B00B5VXMRG)
- [ ] [Calmcode: logging](https://calmcode.io/logging/introduction.html)
- [ ] [Calmcode: tqdm](https://calmcode.io/tqdm/making-a-progress-bar.html)
- [ ] [Calmcode: virtualenv](https://calmcode.io/virtualenv/intro.html)
- [ ] [Calmcode: ray](https://calmcode.io/ray/introduction.html)
- [ ] [Codecademy: Learn Python](https://www.codecademy.com/learn/learn-python)
- [ ] [Cognitiveclass.ai: Python for Data Science](https://cognitiveclass.ai/courses/python-for-data-science)
- [ ] [Doc: Python Lifecycle Training](https://sp-fm.github.io/python-lifecycle-training/index.html)
- [ ] [Datacamp: Python for R Users](https://www.datacamp.com/courses/python-for-r-users)
- [ ] [Datacamp: Python for Spreadsheet Users](https://www.datacamp.com/courses/python-for-spreadsheet-users)
- [ ] [Datacamp: Python for MATLAB Users](https://www.datacamp.com/courses/python-for-matlab-users)
- [ ] [Datacamp: Importing Data in Python (Part 1)](https://www.datacamp.com/courses/importing-data-in-python-part-1)
- [ ] [Datacamp: Intermediate Python for Data Science](https://www.datacamp.com/courses/intermediate-python-for-data-science)
- [ ] [Datacamp: Python Data Science Toolbox (Part 1)](https://www.datacamp.com/courses/python-data-science-toolbox-part-1)
- [ ] [Datacamp: Python Data Science Toolbox (Part 2)](https://www.datacamp.com/courses/python-data-science-toolbox-part-2)
- [ ] [Datacamp: Intro to Python for Finance](https://www.datacamp.com/courses/intro-to-python-for-finance)
- [ ] [Datacamp: Writing Efficient Python Code](https://www.datacamp.com/courses/writing-efficient-python-code)
- [ ] [Datacamp: Writing Functions in Python](https://www.datacamp.com/courses/writing-functions-in-python)
- [ ] [Datacamp: Working with Dates and Times in Python](https://www.datacamp.com/courses/working-with-dates-and-times-in-python)
- [ ] [Datacamp: Object-Oriented Programming in Python](https://datacamp.com/courses/object-oriented-programming-in-python)
- [ ] [edX: Introduction to Python for Data Science](https://www.edx.org/course/introduction-python-data-science-microsoft-dat208x-7)
- [ ] [edX: Programming with Python for Data Science](https://www.edx.org/course/programming-python-data-science-microsoft-dat210x-5)
- [ ] [Google's Python Class](https://developers.google.com/edu/python/)
- [ ] [Treehouse: Python Basics](https://teamtreehouse.com/library/python-basics)
- [ ] [TheNewBoston: Python Programming Tutorials](https://www.youtube.com/watch?v=4Mf0h3HphEA&list=PLEA1FEF17E1E5C0DA)
- [ ] [Udacity: Introduction to Python Programming](https://www.udacity.com/course/introduction-to-python--ud1110)
- [ ] [Udacity: Programming Foundations with Python](https://www.udacity.com/course/programming-foundations-with-python--ud036)
- [ ] [Youtube: Python 3 Programming Tutorial - Regular Expressions / Regex with re](https://www.youtube.com/watch?v=sZyAn2TW7GY)
- [ ] [Youtube: Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)](https://www.youtube.com/watch?v=K8L6KVGG-7o)
- [ ] [Youtube: Tutorial: Sebastian Witowski - Modern Python Developer's Toolkit](https://www.youtube.com/watch?v=WkUBx3g2QfQ&feature=youtu.be)
- [ ] [Youtube: Python Concurrency and Multithreading](https://advanced-python.namespace.im/python-concurrency-and-multithreading/)
- [ ] [Youtube: Aaron Richter- Parallel Processing in Python| PyData Global 2020](https://www.youtube.com/watch?v=eJyjB3cNIB0&feature=youtu.be)
- [ ] [Youtube: The Clean Architecture in Python](https://www.youtube.com/watch?v=DJtef410XaM)

### Be familiar with compiled languages
- [ ] [Codecademy: Learn Java](https://www.codecademy.com/learn/learn-java)
- [ ] [Udacity: C++ For Programmers](https://www.udacity.com/course/c-for-programmers--ud210)
- [ ] [Udacity: Java Programming Basics](https://www.udacity.com/course/java-programming-basics--ud282)

### Have a general understanding of other parts of the stack
- [ ] [Book: Refactoring UI](https://refactoringui.com/book/)
- [ ] [Codecademy: Learn HTML](https://www.codecademy.com/learn/learn-html)
- [ ] [Codecademy: Learn Color Design](https://www.codecademy.com/learn/learn-color-design)
- [ ] [Codecademy: Learn SASS](https://www.codecademy.com/learn/learn-sass)
- [ ] [Codecademy: Make a website](https://www.codecademy.com/en/courses/make-a-website)
- [ ] [Codecademy: Learn ReactJS: Part I](https://www.codecademy.com/learn/react-101)
- [ ] [Codecademy: Learn ReactJS: Part II](https://www.codecademy.com/learn/react-102)
- [ ] [Codecademy: Learn JavaScript](https://www.codecademy.com/learn/learn-javascript)
- [ ] [Codecademy: Jquery Track](https://www.codecademy.com/learn/learn-jquery)
- [ ] [Codecademy: Learn Ruby](https://www.codecademy.com/learn/learn-ruby)
- [ ] [Code School: Fundamentals of Design](https://www.pluralsight.com/courses/code-school-fundamentals-of-design)
- [ ] [Code School: Blasting Off with Bootstrap](https://www.pluralsight.com/courses/code-school-blasting-off-with-bootstrap)
- [ ] [(ES6) - Beau teaches JavaScript](https://www.youtube.com/watch?v=1mgLWu69ijU&list=PLWKjhJtqVAbljtmmeS0c-CEl2LdE-eR_F)
- [ ] [Pluralsight: UX Fundamentals](https://www.pluralsight.com/courses/ux-fundamentals-2426)
- [ ] [Pluralsight: HTML, CSS, and JavaScript: The Big Picture](https://app.pluralsight.com/library/courses/html-css-javascript-big-picture)
- [ ] [Pluralsight: CSS Positioning](https://www.pluralsight.com/courses/css-positioning-1834)
- [ ] [Pluralsight: Introduction to CSS](https://www.pluralsight.com/courses/css-intro)
- [ ] [Pluralsight: CSS: Specificity, the Box Model, and Best Practices](https://app.pluralsight.com/interactive-courses/detail/c580b092-d94a-4ed8-8d2a-2f4d0b76f99f)
- [ ] [Pluralsight: CSS: Using Flexbox for Layout](https://app.pluralsight.com/interactive-courses/detail/a089d0a5-4a4c-4c4e-b883-c1bc64009619)
- [ ] [Pluralsight: Using The Chrome Developer Tools](https://www.pluralsight.com/courses/chrome-developer-tools)
- [ ] [Thoughtbot: Design for Developers](https://thoughtbot.com/upcase/design-for-developers)
- [ ] [Treehouse: HTML](https://teamtreehouse.com/library/html)
- [ ] [Treehouse: Javascript Booleans](https://teamtreehouse.com/library/javascript-booleans)
- [ ] [Udacity: ES6 - JavaScript Improved](https://www.udacity.com/course/es6-javascript-improved--ud356)
- [ ] [Udacity: Intro to Javascript](https://www.udacity.com/course/intro-to-javascript--ud803)
- [ ] [Udacity: Object Oriented JS 1](https://www.udacity.com/course/object-oriented-javascript--ud015)
- [ ] [Udacity: Object Oriented JS 2](https://www.udacity.com/course/object-oriented-javascript--ud711)
- [ ] [Udemy: Understanding Typescript](https://www.udemy.com/understanding-typescript/)

### Be familiar with fundamental Computer Science concepts
- [ ] [Article: Asymptotic Analysis Explained with Pokémon: A Deep Dive into Complexity Analysis](https://www.freecodecamp.org/news/asymptotic-analysis-explained-with-pokemon-a-deep-dive-into-complexity-analysis-8bf4396804e0/)
- [ ] [Book: Grokking Algorithms](https://www.manning.com/books/grokking-algorithms) 
- [ ] [Codecademy: Big O](https://www.codecademy.com/courses/big-o/0/1 )
- [ ] [Crashcourse: Computer Science](https://www.youtube.com/playlist?list=PL8dPuuaLjXtNlUrzyH5r6jN9ulIgZBpdo)
- [ ] [Khan Academy: Data Structures](https://www.khanacademy.org/computing/computer-science/algorithms)
- [ ] [Udacity: Intro to Algorithms](https://www.udacity.com/course/intro-to-algorithms--cs215)
- [ ] [Udacity: Intro to Computer Science](https://www.udacity.com/course/intro-to-computer-science--cs101)
- [ ] [Udacity: Intro to Theoretical Computer Science](https://www.udacity.com/courses/cs313)
- [ ] [Udacity: Programming Languages](https://www.udacity.com/course/programming-languages--cs262)
- [ ] [Udacity: Networking for Web Developers](https://www.udacity.com/course/networking-for-web-developers--ud256)

### Be able to apply proper software engineering process
- [ ] [Pluralsight: Security Awareness: Basic Concepts and Terminology](https://app.pluralsight.com/library/courses/security-awareness-basic-concepts-terminology)
- [ ] [Pluralsight: Secure Software Development](https://www.pluralsight.com/courses/software-development-secure)
- [ ] [Pluralsight: Clean Architecture: Patterns, Practices, and Principles](https://www.pluralsight.com/courses/clean-architecture-patterns-practices-principles)
- [ ] [Thoughtbot: Software Development Process](https://thoughtbot.com/upcase/the-playbook-video-edition)
- [ ] [Thoughtbot: Refactoring](https://thoughtbot.com/upcase/refactoring)
- [ ] [Udacity: Design of Computer Programs](https://www.udacity.com/course/design-of-computer-programs--cs212)
- [ ] [Udacity: Product Design](https://www.udacity.com/course/product-design--ud509)
- [ ] [Udacity: Rapid Prototyping](https://www.udacity.com/course/rapid-prototyping--ud723)
- [ ] [Udacity: Software Architecture and Design](https://www.udacity.com/course/software-architecture-design--ud821)
- [ ] [Udacity: Software Development Process](https://www.udacity.com/course/software-development-process--ud805)
- [ ] [Udacity: Full Stack Foundations](https://www.udacity.com/course/full-stack-foundations--ud088)

### Be able to efficiently use a text editor
- [ ] [Article: Work remotely with PyCharm, TensorFlow and SSH](https://medium.com/@erikhallstrm/work-remotely-with-pycharm-tensorflow-and-ssh-c60564be862d)
- [ ] [Article: Python remote debugging with PyCharm, CUDA, and Conda](https://medium.com/@n1r44/python-remote-debugging-with-pycharm-cuda-and-conda-87d27695634c)
- [ ] [Article: How To Use Visual Studio Code for Remote Development via the Remote-SSH Plugin](https://www.digitalocean.com/community/tutorials/how-to-use-visual-studio-code-for-remote-development-via-the-remote-ssh-plugin)
- [ ] [Article: Docker as Remote Interpreter for PyCharm Professional](https://www.inovex.de/blog/docker-remote-interpreter-pycharm-professional/)
- [ ] [Learn Enough Text Editor to Be Dangerous](https://www.learnenough.com/text-editor-tutorial)
- [ ] [Mastering Pycharm](https://www.amazon.com/Mastering-PyCharm-Quazi-Nafiul-Islam-ebook/dp/B00YSIKR3A)
- [ ] [Video: Pycharm Tips](https://www.jetbrains.com/pycharm/guide/tips/)
- [ ] [Youtube: Productive pytest with PyCharm](https://www.youtube.com/watch?v=ixqeebhUa-w)
- [ ] [Youtube: Getting Started with Python in Visual Studio Code](https://www.youtube.com/watch?v=7EXd4_ttIuw)
- [ ] [Youtube: 42 PyCharm Tips and Tricks](https://www.youtube.com/watch?v=NoDx0MEESDw&list=PLQ176FUIyIUbDwdvWZNuB7FrCc6hHregy&index=35)
- [ ] [Youtube: Pycharm Quick Tips & Tricks](https://www.youtube.com/playlist?list=PLCTHcU1KoD99eyuXqUJHZy90-9jU2H2Y2)
	- [ ] [How to use Live Edit to edit HTML&CSS in PyCharm](https://www.youtube.com/watch?v=xf90Ko64hRc) `0:00:49`
	- [ ] [How to let PyCharm install and generate the imports while you write a symbol](https://www.youtube.com/watch?v=ulT1LSWFqqE) `0:00:35`
	- [ ] [How to enforce One Import Per Line in PyCharm](https://www.youtube.com/watch?v=XB4SnJ4XmUs) `0:00:39`
	- [ ] [How to optimize imports in PyCharm](https://www.youtube.com/watch?v=lNm5ISUZqCE) `0:01:15`
	- [ ] [How to use the navigation bar to move around your project tree in PyCharm](https://www.youtube.com/watch?v=JQE95x7t4aU) `0:01:09`
	- [ ] [How to use Recent Files to open the tool window in PyCharm](https://www.youtube.com/watch?v=ITV0HRKYnXI) `0:00:43`
	- [ ] [How to navigate to symbol using the keyboard in PyCharm](https://www.youtube.com/watch?v=1QoP0U8xg98) `0:00:41`
	- [ ] [How to navigate the cursor position back and forth in PyCharm](https://www.youtube.com/watch?v=_i2gYYfC9H0) `0:00:48`
	- [ ] [How to use the 'find action' shortcut in PyCharm](https://www.youtube.com/watch?v=ESYmaaX5Tzg) `0:01:40`
	- [ ] [How to activate the navigation bar in PyCharm](https://www.youtube.com/watch?v=-uyCSK6gvp4) `0:01:36`
	- [ ] [How to navigate to file using the keyboard in PyCharm](https://www.youtube.com/watch?v=0NFMM143Y5w) `0:00:58`
	- [ ] [How to open a file using the keyboard in PyCharm](https://www.youtube.com/watch?v=37n-a4mg36Q) `0:01:03`
	- [ ] [How to use speed search to navigate files in PyCharm](https://www.youtube.com/watch?v=i9l5YuDgJOw) `0:01:07`
	- [ ] [How to activate the navigation bar and create a file in PyCharm](https://www.youtube.com/watch?v=h6s20RMLIXk) `0:01:09`
	- [ ] [How to use the find in path dialog in PyCharm](https://www.youtube.com/watch?v=AlFEEDMpW8c) `0:00:50`
	- [ ] [How to use drag-n-drop to create a SQLite database in PyCharm](https://www.youtube.com/watch?v=U2sAgysf1Hc) `0:00:40`
	- [ ] [How to evaluate expressions during debugging in PyCharm](https://www.youtube.com/watch?v=zoSKHJdrSfI) `0:01:13`
	- [ ] [How to add conditions to your breakpoints in PyCharm](https://www.youtube.com/watch?v=aIlXb2QpxXc) `0:01:33`
	- [ ] [How to use refactor to rename a file and its references in PyCharm](https://www.youtube.com/watch?v=IqPm9RYTNBM) `0:01:06`
	- [ ] [How to quickly view parameter information in PyCharm](https://www.youtube.com/watch?v=hfWhDwp7zz8) `0:00:38`
	- [ ] [How to view arguments and documentation without interrupting your flow](https://www.youtube.com/watch?v=etvFP6kXsmo) `0:01:08`
	- [ ] [How to make and extend selections in PyCharm](https://www.youtube.com/watch?v=mvZg7TaRjCM) `0:00:39`
	- [ ] [How to reformat your code in PyCharm](https://www.youtube.com/watch?v=0WfDm3KQ-Cw) `0:00:46`
	- [ ] [How to smart-add a new line in PyCharm](https://www.youtube.com/watch?v=pHmv6RzKrDk) `0:01:03`
	- [ ] [How to split the screen in PyCharm](https://www.youtube.com/watch?v=VcqbOtn1s0w) `0:01:35`
	- [ ] [How to get PyCharm adding fields in a constructor for you](https://www.youtube.com/watch?v=eJW507pl4qk) `0:00:47`
	- [ ] [How to use refactoring to rename symbols in PyCharm](https://www.youtube.com/watch?v=cJ0tP9kxoYk) `0:01:13`
	- [ ] [How to avoid disasters by using the local history in PyCharm](https://www.youtube.com/watch?v=hoCkDQCfjwM) `0:01:21`
	- [ ] [How to run your project from the keyboard in PyCharm](https://www.youtube.com/watch?v=oSEMKeUVjlE) `0:01:21`
	- [ ] [How to disable tabs in PyCharm](https://www.youtube.com/watch?v=k7P__ZhUv-g) `0:00:55`
	- [ ] [How to speed up coverage in PyCharm](https://www.youtube.com/watch?v=OCH_gPfrvzw) `0:01:34`
	- [ ] [How to enable auto-run for your tests in PyCharm](https://www.youtube.com/watch?v=4kHncS_gPX0) `0:01:45`
	- [ ] [How to run a single test in PyCharm](https://www.youtube.com/watch?v=-3SS0s-Y420) `0:01:08`
	- [ ] [How to use the gutter to quickly spot missing tests in PyCharm](https://www.youtube.com/watch?v=HQ_bqOBXZ5w) `0:01:06`
	- [ ] [How to reword a commit message in PyCharm](https://www.youtube.com/watch?v=3JSyNP1JRxA) `0:00:49`
	- [ ] [How to do partial commits in PyCharm](https://www.youtube.com/watch?v=NQ-q361wD9Q) `0:01:01`
	- [ ] [How to put a project under version control in PyCharm](https://www.youtube.com/watch?v=36RFkThiZug) `0:01:08`
	- [ ] [How to get started with a repository from GitHub in PyCharm](https://www.youtube.com/watch?v=ukbvdF5wqPQ) `0:01:07`
	- [ ] [How to undo the last commit in PyCharm](https://www.youtube.com/watch?v=R5L5fZi_ldg) `0:00:52`
	- [ ] [How to quickly show your npm scripts in PyCharm](https://www.youtube.com/watch?v=Nv5vO2loJ_c) `0:01:25`
	- [ ] [How to use 'surround with' to insert elements in PyCharm](https://www.youtube.com/watch?v=GK7DEI6zOB8) `0:01:11`

### Be able to communicate and collaborate well
- [ ] [Google: Technical Writing](https://developers.google.com/tech-writing)
- [ ] [Book: Emotional Intelligence](https://www.amazon.com/Emotional-Intelligence-Matter-More-Than/dp/055338371X)
- [ ] [Book: How to Win Friends & Influence People](https://www.amazon.com/How-Win-Friends-Influence-People/dp/0671027034)
- [ ] [Book: Influence: The Psychology of Persuasion](https://www.goodreads.com/book/show/28815.Influence)
- [ ] [Book: Leaders Eat Last: Why Some Teams Pull Together and Others Don't](https://www.amazon.com/Leaders-Eat-Last-Together-Others/dp/1591848016)
- [ ] [Book: Multipliers: How the Best Leaders Make Everyone Smarter](https://www.amazon.com/Multipliers-Best-Leaders-Everyone-Smarter/dp/0061964395)
- [ ] [Book: Soft Skills: The software developer's life manual](https://www.amazon.com/Soft-Skills-software-developers-manual/dp/1617292397)
- [ ] [Book: The New One Minute Manager](https://www.amazon.com/New-One-Minute-Manager-ebook/dp/B00MMG19OG)
- [ ] [Calmcode: Remote Work](https://calmcode.io/remote/introduction.html)
- [ ] [Youtube: Building a psychologically safe workplace | Amy Edmondson | TEDxHGSE](https://youtu.be/LhoLuui9gX8)

### Be familiar with the hiring pipeline
- [ ] [Article: What You Need to Know Before Considering a PhD](https://www.fast.ai/2018/08/27/grad-school/)
- [ ] [Article: Advice to aspiring data scientists: start a blog](http://varianceexplained.org/r/start-blog/)
- [ ] [Article: Systems Design Interview Guide](http://patrickhalina.com/posts/systems-design-interview-guide/)
- [ ] [Article: A Guide to Cold Emailing](http://eugenevinitsky.github.io/posts/2020-12-25-coldemails.html)
- [ ] [Book: Machine Learning Systems Design](https://huyenchip.com/machine-learning-systems-design/toc.html)
- [ ] [Datacamp: Preparing for Statistics Interview Questions in Python](https://www.datacamp.com/courses/preparing-for-statistics-interview-questions-in-python)
- [ ] [Datacamp: Preparing for Coding Interview Questions in Python](https://www.datacamp.com/courses/preparing-for-coding-interview-questions-in-python)
- [ ] [Datacamp: Kaggle Competition](https://www.datacamp.com/courses/winning-a-kaggle-competition-in-python)
- [ ] [Udacity: Optimize your GitHub](https://www.udacity.com/course/optimize-your-github--ud247)
- [ ] [Udacity: Strengthen Your LinkedIn Network & Brand](https://eu.udacity.com/course/strengthen-your-linkedin-network-and-brand--ud242)
- [ ] [Udacity: Data Science Interview Prep](https://www.udacity.com/course/data-science-interview-prep--ud944)
- [ ] [Udacity: Full-Stack Interview Prep](https://www.udacity.com/course/full-stack-interview-prep--ud252)
- [ ] [Udacity: Refresh Your Resume](https://www.udacity.com/course/refresh-your-resume--ud243)
- [ ] [Udacity: Craft Your Cover Letter](https://www.udacity.com/course/craft-your-cover-letter--ud244)
- [ ] [Udacity: Technical Interview](https://www.udacity.com/course/technical-interview--ud513)
- [ ] [Youtube: Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers](https://www.youtube.com/watch?v=733m6qBH-jI)
- [ ] [Youtube: The Importance of Writing in a Tech Career - Eugene Yan](https://youtu.be/vXWGd7olv3c)
- [ ] [Youtube: How to prepare for Machine Learning interviews- Part 1 | Applied AI Course](https://youtu.be/sLAnpLlkh9U?list=LL)
- [ ] [Youtube: How to prepare for Machine Learning interviews- Part 2 | Applied AI Course](https://youtu.be/JxTR1LM4YSA?list=LL)

### Broaden Perspective
- [ ] [Book: Atomic Habits](https://www.amazon.com/Atomic-Habits-Proven-Build-Break/dp/0735211299)
- [ ] [Book: Deep Work](https://www.amazon.com/Deep-Work-Focused-Success-Distracted/dp/1455586692)
- [ ] [Book: Outliers: The Story of Success](https://www.amazon.com/Outliers-Story-Success-Malcolm-Gladwell/dp/0316017930)
- [ ] [Book: Platform: The Art and Science of Personal Branding](https://www.amazon.com/Platform-Art-Science-Personal-Branding/dp/0399581375)
- [ ] [Book: Rich Dad Poor Dad](https://www.amazon.com/Rich-Dad-Poor-Teach-Middle/dp/1543626610)
- [ ] [Book: The Power of Broke](https://www.goodreads.com/book/show/25430691-the-power-of-broke)
- [ ] [Book: The 10X Rule](https://www.amazon.com/10X-Rule-Difference-Between-Success/dp/0470627603)
- [ ] [Book: The Millionaire Fastlane](https://www.amazon.com/Millionaire-Fastlane-Crack-Wealth-Lifetime/dp/0984358102)
- [ ] [Book: The Subtle Art of Not Giving a F\*\*k](https://www.amazon.com/Subtle-Art-Not-Giving-Counterintuitive/dp/0062457713)
- [X] [Calmcode: Pomodoro](https://calmcode.io/pomodoro/introduction.html)
- [ ] [Youtube: Why specializing early doesn't always mean career success | David Epstein](https://www.youtube.com/watch?v=B6lBtiQZSho)
- [ ] [Youtube: Chamath Palihapitiya, Founder and CEO Social Capital, on Money as an Instrument of Change](https://youtu.be/PMotykw0SIk)
- [ ] [Youtube: How to Build a Personal Monopoly with Jack Butcher](https://www.youtube.com/watch?v=VINZMrpPIg8&feature=youtu.be)
- [ ] [Youtube: How to Use Twitter](https://www.youtube.com/watch?v=kDYLQUf8uBw&feature=youtu.be)
- [X] [Youtube: A. Jesse Jiryu Davis - Write an Excellent Programming Blog - PyCon 2016](https://www.youtube.com/watch?v=eHXq-IzlGUE)
- [ ] [Youtube: The Great ML Stagnation (Mark Saroufim and Dr. Mathew Salvaris)](https://www.youtube.com/watch?v=BwhBtvCNwxo)
- [ ] [Youtube: What Machine Learning Can Teach Us About Life: 7 Lessons - Talk Python Live Stream](https://www.youtube.com/watch?v=EE1u1dbPZWQ)