A curated list of free, legitimately accessible books on Deep Learning, Reinforcement Learning, Probabilistic Machine Learning, NLP/LLMs, and mathematical foundations — all made officially available by their own authors or publishers.
Every link points to an official source. This repository doesn't host any files — it only organizes and catalogs what authors already offer for free. See why at the bottom.
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Deep Learning | Ian Goodfellow, Yoshua Bengio, Aaron Courville (MIT Press) | 2016 | deeplearningbook.org |
| Understanding Deep Learning | Simon J.D. Prince | 2023 | udlbook.github.io |
| Deep Learning: Foundations and Concepts | Christopher Bishop, Hugh Bishop | 2024 | bishopbook.com |
| Dive into Deep Learning (D2L) | Zhang, Lipton, Li, Smola | 2023 | d2l.ai |
| The Little Book of Deep Learning | François Fleuret | 2023 (updated 2026) | fleuret.org |
| Neural Networks and Deep Learning | Michael Nielsen | 2015 | neuralnetworksanddeeplearning.com |
| A Brief Introduction to Neural Networks | David Kriesel | — | dkriesel.com |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Reinforcement Learning: An Introduction (2nd ed.) | Richard Sutton, Andrew Barto | 2018 | incompleteideas.net |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Probabilistic Machine Learning: An Introduction | Kevin Murphy | 2022 | probml.github.io |
| Probabilistic Machine Learning: Advanced Topics | Kevin Murphy | 2023 | probml.github.io |
| Bayesian Reasoning and Machine Learning | David Barber | 2012 | web4.cs.ucl.ac.uk |
| Probabilistic Programming & Bayesian Methods for Hackers | Cam Davidson-Pilon | — | dataorigami.net |
| Think Bayes | Allen Downey | 2021 | greenteapress.com |
| Information Theory, Inference, and Learning Algorithms | David MacKay | 2003 | inference.phy.cam.ac.uk |
| Gaussian Processes for Machine Learning | Rasmussen, Williams | 2006 | gaussianprocess.org |
| A Probabilistic Theory of Pattern Recognition | Devroye, Györfi, Lugosi | — | szit.bme.hu |
| Probability Theory: The Logic of Science | E.T. Jaynes | — | bayes.wustl.edu |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| The Elements of Statistical Learning | Hastie, Tibshirani, Friedman | 2009 | stanford.edu |
| An Introduction to Statistical Learning (R & Python) | James, Witten, Hastie, Tibshirani | 2021/2023 | statlearning.com |
| Computer Age Statistical Inference (CASI) | Efron, Hastie | 2016 | hastie.su.domains |
| Foundations of Machine Learning | Mohri, Rostamizadeh, Talwalkar | 2018 | cs.nyu.edu |
| Understanding Machine Learning | Shalev-Shwartz, Ben-David | 2014 | cs.huji.ac.il |
| A Course in Machine Learning | Hal Daumé III | — | ciml.info |
| Mining Massive Datasets | Leskovec, Rajaraman, Ullman | — | stanford.edu |
| The Hundred-Page Machine Learning Book | Andriy Burkov | 2019 | themlbook.com |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| RAG + Knowledge Graph Master Course | Addy | 2026 | thequery.in |
| Speech and Language Processing (3rd ed., draft) | Daniel Jurafsky, James Martin | 2026 (active draft) | web.stanford.edu |
| Foundations of Statistical Natural Language Processing | Manning, Schütze | 1999 | nlp.stanford.edu |
| An Introduction to Information Retrieval | Manning, Raghavan, Schütze | 2008 | nlp.stanford.edu |
| AI Agent Evaluation | Hallie Ren | 2026 | hallieren.github.io |
| Research, Rewritten: Using AI to Produce Knowledge You Can Trust | Hallie Ren | 2026 | hallieren.github.io |
| NLTK Book | Bird, Klein, Loper | — | nltk.org |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Mathematics for Machine Learning | Deisenroth, Faisal, Ong | 2020 | mml-book.github.io |
| The Matrix Cookbook | Petersen, Pedersen | — | uwaterloo.ca |
| Convex Optimization | Boyd, Vandenberghe | 2004 | stanford.edu |
| Linear Algebra Done Wrong | Sergei Treil | — | brown.edu |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Machine Learning Systems (Vol. I & II) | Harvard-Edge | 2025–2026 | mlsysbook.ai |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Computer Vision: Algorithms and Applications (2nd ed.) | Richard Szeliski | 2022 | szeliski.org/Book (simple sign-up, free download) |
| Computer Vision: Models, Learning, and Inference | Simon J.D. Prince | 2012 | computervisionmodels.com |
| Foundations of Computer Vision | Antonio Torralba, Phillip Isola, William T. Freeman | 2024 | visionbook.mit.edu |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Deep Generative Modeling (2nd ed.) | Jakub M. Tomczak | 2024 | jmtomczak.github.io — covers mixture models, autoregressive models, flows, VAEs, GANs, score-based/diffusion, energy-based models, and LLMs |
| An Introduction to Variational Autoencoders | Diederik P. Kingma, Max Welling | 2019 | arxiv.org |
| The Principles of Diffusion Models | Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon | 2025 | arxiv.org |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Causal Inference: What If | Miguel Hernán, James Robins | 2020 (updated) | miguelhernan.org/whatifbook |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Graph Representation Learning | William L. Hamilton | 2020 | cs.mcgill.ca/~wlh/grl_book |
| Deep Learning on Graphs | Yao Ma, Jiliang Tang | 2021 | yaoma24.github.io |
| Graph Neural Networks: Foundations, Frontiers, and Applications | Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao | 2022 | graph-neural-networks.github.io |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Introduction to AI Safety, Ethics, and Society | Dan Hendrycks (Center for AI Safety) | 2024 | aisafetybook.com — open access, includes a free companion course |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Reinforcement Learning: Theory and Algorithms | Agarwal, Brantley, Jiang, Kakade, Sun | 2022 (continuously updated) | rltheorybook.github.io |
| Reinforcement Learning and Optimal Control | Dimitri Bertsekas | 2019 | author's site (ASU) |
| A Course in Reinforcement Learning (2nd ed.) | Dimitri Bertsekas | 2023/2025 | author's site (ASU) |
Contributions are welcome! Before opening a PR, read CONTRIBUTING.md. Quick rules:
This repository does not host any files. It works as a catalog/index, pointing to where authors themselves make their books freely available. This approach:
If a link breaks, please open an issue.
Made with 📖 for the global AI/ML community.
Curated by Marcos Cruz — feel free to connect on LinkedIn.
69 followers · starred Jul 2026
A curated list of free, legitimately accessible books on Deep Learning, Reinforcement Learning, Probabilistic Machine Learning, NLP/LLMs, and mathematical foundations — all made officially available by their own authors or publishers.
Every link points to an official source. This repository doesn't host any files — it only organizes and catalogs what authors already offer for free. See why at the bottom.
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Deep Learning | Ian Goodfellow, Yoshua Bengio, Aaron Courville (MIT Press) | 2016 | deeplearningbook.org |
| Understanding Deep Learning | Simon J.D. Prince | 2023 | udlbook.github.io |
| Deep Learning: Foundations and Concepts | Christopher Bishop, Hugh Bishop | 2024 | bishopbook.com |
| Dive into Deep Learning (D2L) | Zhang, Lipton, Li, Smola | 2023 | d2l.ai |
| The Little Book of Deep Learning | François Fleuret | 2023 (updated 2026) | fleuret.org |
| Neural Networks and Deep Learning | Michael Nielsen | 2015 | neuralnetworksanddeeplearning.com |
| A Brief Introduction to Neural Networks | David Kriesel | — | dkriesel.com |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Reinforcement Learning: An Introduction (2nd ed.) | Richard Sutton, Andrew Barto | 2018 | incompleteideas.net |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Probabilistic Machine Learning: An Introduction | Kevin Murphy | 2022 | probml.github.io |
| Probabilistic Machine Learning: Advanced Topics | Kevin Murphy | 2023 | probml.github.io |
| Bayesian Reasoning and Machine Learning | David Barber | 2012 | web4.cs.ucl.ac.uk |
| Probabilistic Programming & Bayesian Methods for Hackers | Cam Davidson-Pilon | — | dataorigami.net |
| Think Bayes | Allen Downey | 2021 | greenteapress.com |
| Information Theory, Inference, and Learning Algorithms | David MacKay | 2003 | inference.phy.cam.ac.uk |
| Gaussian Processes for Machine Learning | Rasmussen, Williams | 2006 | gaussianprocess.org |
| A Probabilistic Theory of Pattern Recognition | Devroye, Györfi, Lugosi | — | szit.bme.hu |
| Probability Theory: The Logic of Science | E.T. Jaynes | — | bayes.wustl.edu |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| The Elements of Statistical Learning | Hastie, Tibshirani, Friedman | 2009 | stanford.edu |
| An Introduction to Statistical Learning (R & Python) | James, Witten, Hastie, Tibshirani | 2021/2023 | statlearning.com |
| Computer Age Statistical Inference (CASI) | Efron, Hastie | 2016 | hastie.su.domains |
| Foundations of Machine Learning | Mohri, Rostamizadeh, Talwalkar | 2018 | cs.nyu.edu |
| Understanding Machine Learning | Shalev-Shwartz, Ben-David | 2014 | cs.huji.ac.il |
| A Course in Machine Learning | Hal Daumé III | — | ciml.info |
| Mining Massive Datasets | Leskovec, Rajaraman, Ullman | — | stanford.edu |
| The Hundred-Page Machine Learning Book | Andriy Burkov | 2019 | themlbook.com |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| RAG + Knowledge Graph Master Course | Addy | 2026 | thequery.in |
| Speech and Language Processing (3rd ed., draft) | Daniel Jurafsky, James Martin | 2026 (active draft) | web.stanford.edu |
| Foundations of Statistical Natural Language Processing | Manning, Schütze | 1999 | nlp.stanford.edu |
| An Introduction to Information Retrieval | Manning, Raghavan, Schütze | 2008 | nlp.stanford.edu |
| AI Agent Evaluation | Hallie Ren | 2026 | hallieren.github.io |
| Research, Rewritten: Using AI to Produce Knowledge You Can Trust | Hallie Ren | 2026 | hallieren.github.io |
| NLTK Book | Bird, Klein, Loper | — | nltk.org |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Mathematics for Machine Learning | Deisenroth, Faisal, Ong | 2020 | mml-book.github.io |
| The Matrix Cookbook | Petersen, Pedersen | — | uwaterloo.ca |
| Convex Optimization | Boyd, Vandenberghe | 2004 | stanford.edu |
| Linear Algebra Done Wrong | Sergei Treil | — | brown.edu |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Machine Learning Systems (Vol. I & II) | Harvard-Edge | 2025–2026 | mlsysbook.ai |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Computer Vision: Algorithms and Applications (2nd ed.) | Richard Szeliski | 2022 | szeliski.org/Book (simple sign-up, free download) |
| Computer Vision: Models, Learning, and Inference | Simon J.D. Prince | 2012 | computervisionmodels.com |
| Foundations of Computer Vision | Antonio Torralba, Phillip Isola, William T. Freeman | 2024 | visionbook.mit.edu |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Deep Generative Modeling (2nd ed.) | Jakub M. Tomczak | 2024 | jmtomczak.github.io — covers mixture models, autoregressive models, flows, VAEs, GANs, score-based/diffusion, energy-based models, and LLMs |
| An Introduction to Variational Autoencoders | Diederik P. Kingma, Max Welling | 2019 | arxiv.org |
| The Principles of Diffusion Models | Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon | 2025 | arxiv.org |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Causal Inference: What If | Miguel Hernán, James Robins | 2020 (updated) | miguelhernan.org/whatifbook |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Graph Representation Learning | William L. Hamilton | 2020 | cs.mcgill.ca/~wlh/grl_book |
| Deep Learning on Graphs | Yao Ma, Jiliang Tang | 2021 | yaoma24.github.io |
| Graph Neural Networks: Foundations, Frontiers, and Applications | Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao | 2022 | graph-neural-networks.github.io |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Introduction to AI Safety, Ethics, and Society | Dan Hendrycks (Center for AI Safety) | 2024 | aisafetybook.com — open access, includes a free companion course |
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Reinforcement Learning: Theory and Algorithms | Agarwal, Brantley, Jiang, Kakade, Sun | 2022 (continuously updated) | rltheorybook.github.io |
| Reinforcement Learning and Optimal Control | Dimitri Bertsekas | 2019 | author's site (ASU) |
| A Course in Reinforcement Learning (2nd ed.) | Dimitri Bertsekas | 2023/2025 | author's site (ASU) |
Contributions are welcome! Before opening a PR, read CONTRIBUTING.md. Quick rules:
This repository does not host any files. It works as a catalog/index, pointing to where authors themselves make their books freely available. This approach:
If a link breaks, please open an issue.
Made with 📖 for the global AI/ML community.
Curated by Marcos Cruz — feel free to connect on LinkedIn.
69 followers · starred Jul 2026
