Dive into Deep Learning (with PyTorch)

by Aston Zhang, Zachary Lipton, Mu Li, Alexander Smola

Combines rigorous mathematical foundations with hands-on, executable code examples in a free, interactive format that takes you from deep learning fundamentals to state-of-the-art techniques, making it ideal whether you're a student, researcher, or practitioner looking to truly understand and implement neural networks.

Chapter 1: Introduction
Chapter 2: Preliminaries
Chapter 3: Linear Regression
Chapter 4: Linear Classification
Chapter 5: Multilayer Perceptrons
Chapter 6: Builder's Guide
Chapter 7: Convolutional Neural Networks
Chapter 8: Modern Convolutional Networks
Chapter 9: Recurrent Neural Networks
Chapter 10: Modern Recurrent Networks
Chapter 11: Attention Mechanisms and Transformers
Chapter 12: Optimization
Chapter 13: Computational Performance
Chapter 14: Computer Vision
Chapter 15: Natural Language Processing: Pretraining
Chapter 16: Natural Language Processing: Applications
Chapter 17: Reinforcement Learning
Chapter 18: Gaussian Processes
Chapter 19: Hyperparameter Optimization
Chapter 20: Generative Adversarial Networks
Chapter 21: Recommender Systems
Appendix: Mathematics for Deep Learning
Appendix: Tools for Deep Learning