Reading list

The books worth reading

The real foundations: AI, machine learning, deep learning, LLMs, the math underneath, and what it all means. Many are free online.

Math foundations

Mathematics for Machine Learning

Free

Deisenroth, Faisal, Ong

The exact math you need for ML, nothing more.

Linear Algebra Done Right

Free

Sheldon Axler

Linear algebra built on understanding, not determinants.

Introduction to Probability

Free

Blitzstein & Hwang

Probability intuition that actually sticks.

Machine learning

The Elements of Statistical Learning

Free

Hastie, Tibshirani, Friedman

The classic reference for statistical ML.

Probabilistic Machine Learning

Free

Kevin Murphy

Modern, comprehensive, and freely online.

Hands-On Machine Learning

Aurélien Géron

The most practical way in, with real code.

Deep learning

Deep Learning

Free

Goodfellow, Bengio, Courville

The foundational deep-learning textbook.

Dive into Deep Learning

Free

Zhang, Lipton, Li, Smola

Interactive, runnable, framework-complete.

Neural Networks and Deep Learning

Free

Michael Nielsen

The friendliest from-scratch intro.

LLMs and transformers

Speech and Language Processing

Free

Jurafsky & Martin

The NLP bible, updated for the LLM era.

Build a Large Language Model (From Scratch)

Sebastian Raschka

Build a working LLM line by line.

Natural Language Processing with Transformers

Tunstall, von Werra, Wolf

Hands-on transformers with Hugging Face.

Reinforcement learning

Reinforcement Learning: An Introduction

Free

Sutton & Barto

The definitive RL text, free from the authors.

AI and society

Human Compatible

Stuart Russell

How to keep AI aligned with human interests.

The Alignment Problem

Brian Christian

A clear, human story of the hardest problem in AI.

Atlas of AI

Kate Crawford

The real material and political costs of AI.