Books

Pattern Recognition and Machine Learning

Probabilistic Machine Learning: An Introduction

Probabilistic Machine Learning: Advanced Topics

Machine Learning

Deep Learning

Deep Learning: Foundations and Concepts

Understanding Deep Learning

Reinforcement Learning: An Introduction

Foundations of Computer Vision

Recommender Systems: The Textbook

Introduction to Data Mining

Information Theory: From Coding to Learning

Information Theory, Inference, and Learning Algorithms

All of Statistics: A Concise Course in Statistical Inference

High-Dimensional Probability: An Introduction with Applications in Data Science

The Elements of Statistical Learning

An Introduction to Statistical Learning

Statistical Rethinking: A Bayesian Course with Examples in R and Stan

Regression and Other Stories

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Active Inference

Thinking About Statistics

Structure and Interpretation of Computer Programs

The Elements of Computing Systems

Operating Systems: Three Easy Pieces

Code: The Hidden Language of Computer Hardware and Software

Crafting Interpreters

Programming Massively Parallel Processors

Digital Communications

Satellite Communications

Designing Data-Intensive Applications

Database Internals

Fluent Python

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Interpretable Machine Learning

Machine Learning Mastery With Python

Machine Learning with PyTorch and Scikit-Learn

Deep Learning with Python

Natural Language Processing with Transformers

Probabilistic Deep Learning

Advanced Algorithms and Data Structures

Grokking Algorithms

Grokking Data Structures

Grokking Reinforcement Learning

Grokking Deep Learning

Cassandra: The Definitive Guide

Approaching (Almost) Any Machine Learning Problem