LearningAI

The online resources/tutorials to learn AI. ⭐ represents importance. 💥 represents difficulty

Math

• The Matrix Cookbook ⭐⭐ 💥💥

Collections

• Hello Interview ⭐⭐⭐ 💥💥
• Educative Grokking the Machine Learning Interview ⭐⭐⭐ 💥
• Educative Machine Learning System Design ⭐⭐⭐ 💥
• ML System Design Post in LinkedIn ⭐⭐⭐
• ML System Design of Large Tech Companies ⭐⭐⭐
• Systems Design Interview Guide ⭐⭐ 💥
• machine-learning-interviews ⭐⭐ 💥💥
• machine-learning-interview ⭐⭐ 💥
• Landmark Papers in Machine Learning ⭐ 💥
• Papers on Explainable Artificial Intelligence ⭐ 💥
• Must-read papers on GNN ⭐ 💥
• Cracking the Data Science Interview ⭐⭐⭐ 💥
• Machine Learning Design Patterns ⭐⭐ 💥💥
• Designing Machine Learning Systems ⭐⭐ 💥💥

Theory

• Probability cheatsheet ⭐⭐ 💥
• Statistics cheatsheet ⭐⭐ 💥
• Linear algebra explained in four pages ⭐⭐ 💥
• Calculus Cheat Sheet ⭐⭐ 💥
• probability_cheatsheet v2 ⭐⭐ 💥
• mathematics-for-machine-learning -- Andrew Ng ⭐⭐ 💥💥
• Math for ML -- Tübingen Machine Learning ⭐ 💥💥💥
• Math for DL -- Tübingen Machine Learning ⭐ 💥💥💥

Exp

• Prompt Engineering Guide ⭐⭐ 💥
• Google Tuning Playbook ⭐⭐⭐ 💥💥
• Intermediate Python ⭐⭐⭐ 💥💥
• Approaching (Almost) Any Machine Learning Problem ⭐⭐ 💥
• Everything about Distributed Training and Efficient Finetuning ⭐⭐ 💥
• Datasets and Benchmarks Best Practices ⭐
• Optuna ⭐⭐ 💥

Feature Engineering

• Feature Selection Techniques in Machine Learning ⭐
• Feature Selection ⭐ 💥

General

• Dive into Deeplearning ⭐⭐⭐ 💥
• Annotated Research Paper Implementations ⭐⭐ 💥💥
• Grokking the Machine Learning Interview ⭐⭐⭐ 💥
• Machine Learning System Design ⭐⭐⭐ 💥
• Practical Deep Learning ⭐⭐⭐ 💥
• Stanford CS 221 ― Artificial Intelligence ⭐⭐ 💥💥
• Stanford CS 230 ― Deep Learning ⭐⭐ 💥💥
• Serrano Academy ⭐⭐⭐ 💥

ML

• ML CheatSheets ⭐
• Google ML Course ⭐⭐ 💥
• SuperVIP Cheatsheet: Machine Learning ⭐ 💥
• Stanford CS 229 ― Machine Learning ⭐⭐ 💥💥
• StatQuest with Josh Starmer ⭐⭐ 💥

NLP

• Practical NLP with Python ⭐ 💥
• CS324 - Large Language Models ⭐ 💥
• Hugging Face NLP Course ⭐⭐ 💥
• NLP CheatSheet: Master NLP ⭐
• ML CHEATSHEET: A mind map for NLP ⭐
• NLP Interview Notes ⭐⭐⭐ 💥

CV

• Computer Vision CheatSheet ⭐
• An Introduction to Vision Language Models ⭐⭐⭐

RL

• OpenAI Spinning Up ⭐⭐⭐ 💥💥
• Reinforcement Learning CheatSheet ⭐
• Introduction to Reinforcement Learning ⭐ 💥

Graph

RecSys

• Recommendation Systems on Google Cloud ⭐⭐⭐ 💥
• FunRec Documents ⭐⭐ 💥💥
• FunRec github ⭐ 💥💥

GM

• Tutorial on Diffusion Models for Imaging and Vision ⭐ 💥

DM

• Data Mining CheatSheet ⭐

LLM

• Large Language Model Cheat Sheet ⭐
• LLM Visualization ⭐
• A Survey of LLM ⭐
• LLM Interview Notes ⭐⭐⭐ 💥
• Educative LLM Vector Database ⭐ 💥

RAI

• Interpretable ML ⭐⭐ 💥

Meta

• Metric Learning ⭐ 💥

MLOps

• Git Tutorial ⭐⭐ 💥

ML System Design

• CS 329S: Machine Learning Systems Design ⭐⭐ 💥💥
• Grokking the Machine Learning Interview ⭐⭐⭐ 💥
• ML System Design Pattern ⭐⭐ 💥💥
• Alex Xu's Book on ML System Design ⭐⭐⭐ 💥💥

Software System Design

• System Design for Interviews and Beyond ⭐⭐⭐ 💥

Cookbook

• How To Get PhD
• Gitlab Cheat Sheet 🆕
• A Cookbook of Self-Supervised Learning 🆕🆕
• Deep Learning: An MIT Press book 🆕
• PyTorch Cheat Sheet 🆕
• The Matrix Cookbook

Blogs

• Lil'Log
• Jay Alammar
• Openai Research
• Jonathan Hui
• Trelis Research

Tutorials

• Building LLMs from the Ground Up: A 3-hour Coding Workshop