This document contains a collection of freely available learning resources that we have found (or think might be) useful, covering a broad range of computer science topics (with a larger focus on AI related topics).
Feel free to add suggestions or feedback as we continue to grow the list! Issues and Pull Requests are more than welcome. Happy studying :)
Applied Deep Learning -- Playlist of over 500 videos by Maziar Raissi covering a vast range of Deep Learning topics, from NN basics, CNNs, RNNs, RL, etc.
Introduction to Probability -- Over 200 lectures from MIT covering the fundamentals of probability.
Mathematics for Computer Science -- Over 100 lectures from MIT covering undergraduate level mathematical fundamentals for CompSci.
Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy -- A nice overview of the Transformer architecture, including a brief historical overview.
Let's build GPT: from scratch, in code, spelled out. -- Code-along video, by Andrej Karpathy of OpenAI, implementing a tiny GPT LLM in Pytorch.
Berkeley Deep RL -- Playlist of the full Deep RL course at Berkeley (99 videos in total) by Sergey Levine.
Deepmind x UCL -- Playlist of 13 lectures covering RL fundamentals by Google Deepmind in colaboration with UCL.
Standford CS234 -- Playlist of 15 lectures from Stanford covering RL fundamentals.
Theory of Computation -- Playlist of 26 lectures of the entirety of the MIT Theory of Computation Fall 2020 course.
Introduction to Algorithms -- Playlist of 48 lectures by MIT for the 2011 course in Introduction to Algorithms.
| Book Title | Author | Link |
|---|---|---|
| Theory of Computation | Jim Hefferon | here |
| Book Title | Author | Link |
|---|---|---|
| Mathematics for Machine Learning | Deisenroth, Faisal, and Ong | here |
| Linear Algebra | Georgi E. Shilov | here |
| Linear Algebra for an undergraduate course | Jim Hefferon | here |
| Introduction to Proofs | Jim Hefferon | here |
| Book Title | Author | Link |
|---|---|---|
| Introduction to Probability | Grinstead and Snell | here |
| Probabilistic Machine Learning | Kevin Murphy | here |
| Book Title | Author | Link |
|---|---|---|
| Deep Learning | Goodfellow, Bengio, and Courville | here |
| Book Title | Author | Link |
|---|---|---|
| Reinforcement Learning An Introduction | Sutton and Barto | here |
| Algorithms for Reinforcement Learning | Csaba Szepesvari | here |
| Bandit Algorithms | Tor Lattimore and Csaba Szepesv´ari | here |
| Book Title | Author | Link |
|---|---|---|
| Information Theory, Inference, and Learning Algorithms | David MacKay | here |
| Book Title | Author | Link |
|---|---|---|
| A Concise Introduction to Models and Methods for Automated Planning | Geffner and Bonet | here |
| Algorithms for Decision Making | Kochenderfer, Wheeler, and Wray | here |
Policy Gradient Algorithms -- post by Lilian Weng explaining policy gradient algorithms in-depth.
Deep Learning Weekly -- Weekly newsletter summarising latest developments in deep learning (including a selection of academic papers.)
Alpha Signal -- Weekly (along with latest news) newletter covering the latest AI research papers, news, and GitHub repositories.