#Neural Networks and Deep Learning
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Deep Learning Textbooks
Deep Learning Tutorials
Deep Learning Courses
Deep Learning Lectures
Deep Learning Podcasts
Deep Learning Packages
Deep Learning Misc
Neural Networks and Deep Learning
by Michael Nielsen
"The book will teach you about: Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data; Deep learning, a powerful set of techniques for learning in neural networks." Uses Python 2.
Deep Learning
by Ian Goodfellow, Yoshua Bengio and Aaron Courville
"The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular." A pdf version appears to be available here: https://github.com/HFTrader/DeepLearningBook/raw/master/DeepLearningBook.pdf
Machine Learning & Deep Learning Tutorials
by Ujjwal Karn
"This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. "
Awesome TensorFlow
by jtoy
A curated list of awesome TensorFlow experiments, libraries, and projects.
Other tags: Tutorials in Python
Not another MNIST tutorial with TensorFlow
by Justin Francis
"Back when TensorFlow was released to the public in November 2015, I remember following TensorFlow’s beginner MNIST tutorial. I blindly copied and pasted all this code into my terminal and some numbers popped out as they should have. I thought, OK, I know there is something amazing happening here, why can I not see it? My goal was to make a MNIST tutorial that was both interactive and visual, and hopefully will teach you a thing or two that others just assume you know."
Other tags: Tutorials in Python, Image Classification
Diving into Machine Learning through TensorFlow - PyCon 2016
by Julia Ferraioli, Amy Unruh, Eli Bixby
"Machine learning can be an intimidating subject. In this session, we'll get practical, hands-on experience with core concepts in machine learning with TensorFlow, an open source deep learning library. We’ll introduce the basics of TensorFlow, including how to ingest and prepare raw data for use, run a variety of algorithms to gain insight from the data, and have some fun with visualization."
Other tags: Tutorials in Python
Introduction to Scikit Flow
by Yuan Tang
"Scikit Flow is a simplified interface for TensorFlow, to get people started on predictive analytics and data mining. It helps smooth the transition from the Scikit-learn world of one-liner machine learning into the more open world of building different shapes of ML models. You can start by using fit/predict and slide into TensorFlow APIs as you are getting comfortable. It’s Scikit-learn compatible so you can also benefit from Scikit-learn features like GridSearch and Pipeline."
Other tags: Tutorials in Python
Neural Networks for Machine Learning
by Geoffrey Hinton
"Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.
This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform.
Please be advised that the course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python)."
Deep Learning Learning Path from Big Data University
by IBM
"In this learning path, you will be able to learn the basic concepts of Neural Networks, Deep Leaning and TensorFlow. Then, you will get hands-on experience in solving problems using Deep Learning. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders."
CS231n: Convolutional Neural Networks for Visual Recognition
by Stanford University
"This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification." Course includes a Python tutorial. Uses Python 2.
Other tags: Tutorials in Python, Image Classification
Learning Machines 101
by Richard M. Golden
The intended audience for this podcast series is the general public and the intended objective of this podcast series is to help popularize and demystify the field of Artificial Intelligence by explaining fundamental concepts in an entertaining manner. However, many advanced topics in artificial intelligence and machine learning will be discussed at a “high-level” so students, scientists, and engineers working in the machine learning area may find this podcast series beneficial for identifying relevant “entry points” into advanced statistical machine learning topics. Relevant references to advanced readings are provided (when applicable) in the show notes for each episode.
Other tags: Machine Learning Podcasts, Reinforcement Learning Podcasts
TensorFlow
"TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system."
Other tags: Python Packages
Theano
"Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently."
Other tags: Python Packages
Keras
"Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research."
Other tags: Python Packages
The Neural Network Zoo
by Fjodor Van Veen
"With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first.
So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Though all of these architectures are presented as novel and unique, when I drew the node structures… their underlying relations started to make more sense."
Yes you should understand backprop
by Andrej Karpathy
"In other words, it is easy to fall into the trap of abstracting away the learning process — believing that you can simply stack arbitrary layers together and backprop will “magically make them work” on your data. So lets look at a few explicit examples where this is not the case in quite unintuitive ways."