Essential AI/ML Books
Theoretical Foundations
Deep Learning
- Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville
- Publisher: MIT Press · 2016
- Free PDF: https:/ww.deeplearningbook.org/
- Covers: backprop, CNNs, RNNs, unsupervised models, mathematical foundations
Pattern Recognition and Machine Learning
- Authors: Christopher M. Bishop
- Publisher: Springer · 2006
- Covers: probabilistic graphical models, Bayesian methods, variational inference
Machine Learning: A Probabilistic Perspective
- Authors: Kevin P. Murphy
- Publisher: MIT Press · 2012
- Covers: classical and probabilistic ML, graphs, Monte Carlo — complete theoretical reference
Probabilistic Machine Learning (vols. 1 and 2)
- Authors: Kevin P. Murphy
- Publisher: MIT Press · 2022/2023
- Free PDF: https:/robml.github.iopml-book
- Covers: modern, expanded version of the previous one; includes deep learning and LLMs
Reinforcement Learning: An Introduction
- Authors: Richard S. Sutton & Andrew G. Barto
- Publisher: MIT Press · 2018 (2nd ed.)
- Free PDF: http:/ncompleteideas.netbookthe-book-2nd.html
- Covers: DP, TD learning, Q-learning, policy gradients — standard RL reference
Mathematics for Machine Learning
- Authors: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
- Publisher: Cambridge University Press · 2020
- Free PDF: https:/ml-book.github.io/
- Covers: linear algebra, calculus, probability, optimization — mathematical foundation
The Elements of Statistical Learning
- Authors: Trevor Hastie, Robert Tibshirani, Jerome Friedman
- Publisher: Springer · 2009 (2nd ed.)
- Free PDF: https:/astie.su.domainsElemStatLearn
- Covers: classical ML: trees, SVMs, boosting, shallow neural networks
Neural Networks and Deep Learning
- Authors: Michael Nielsen
- Free online: http:/euralnetworksanddeeplearning.com/
- Covers: accessible introduction — backprop, CNNs, regularization
Books on LLMs and Modern AI
Build a Large Language Model (From Scratch)
- Authors: Sebastian Raschka
- Publisher: Manning · 2024
- Covers: practical implementation of an LLM from scratch in PyTorch — highly recommended
Designing Machine Learning Systems
- Authors: Chip Huyen
- Publisher: O'Reilly · 2022
- Covers: MLOps, data pipelines, deployment, production monitoring
Natural Language Processing with Transformers
- Authors: Lewis Tunstall, Leandro von Werra, Thomas Wolf
- Publisher: O'Reilly · 2022
- Free PDF: https:/ransformersbook.com/
- Covers: practical fine-tuning with HuggingFace Transformers
The RLHF Book
- Authors: Nathan Lambert
- Free online: https:/lhfbook.com/
- Covers: complete RLHF — reward models, PPO, DPO, alignment
Permanent Online Resources
| Resource | URL | Focus |
|---|---|---|
| Andrej Karpathy — Neural Networks: Zero to Hero | youtube.com/@karpathy | Practical LLM implementation |
| fast.ai — Practical Deep Learning | fast.ai | Applied DL, top-down |
| Stanford CS229 (ML) | cs229.stanford.edu | ML foundations |
| Stanford CS224N (NLP) | cs224n.stanford.edu | NLP and Transformers |
| Stanford CS336 (LLMs) | cs336.stanford.edu | LLMs from scratch (2024) |
| Hugging Face Course | huggingface.co/learn | Practical, HF ecosystem |
| Lilian Weng's Blog | lilianweng.github.io | Excellent technical summaries |
| Sebastian Raschka's Blog | magazine.sebastianraschka.com | LLMs, fine-tuning, papers |