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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 :)

Youtube Resources

Deep Learning

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.

Mathematics & Statistics

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.

LLMs & Transformers

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.

Reinforcement Learning

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.

Computational Theory

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.

Collection of textbooks

Computation Theory

Book Title Author Link
Theory of Computation Jim Hefferon here

Mathematics

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

Statistics & Probability

Book Title Author Link
Introduction to Probability Grinstead and Snell here
Probabilistic Machine Learning Kevin Murphy here

Deep Learning

Book Title Author Link
Deep Learning Goodfellow, Bengio, and Courville here

Reinforcement Learning

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

Information Theory

Book Title Author Link
Information Theory, Inference, and Learning Algorithms David MacKay here

Planning

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

Blog Posts

Reinforcement Learning

Policy Gradient Algorithms -- post by Lilian Weng explaining policy gradient algorithms in-depth.

Email Lists & Newsletters

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.