Table of contents
Chapter 1 - Introduction
Chapter 2 - Supervised learning
Chapter 3 - Shallow neural networks
Chapter 4 - Deep neural networks
Chapter 5 - Loss functions
Chapter 6 - Training models
Chapter 7 - Gradients and initialization
Chapter 8 - Measuring performance
Chapter 9 - Regularization
Chapter 10 - Convolutional networks
Chapter 11 - Residual networks
Chapter 12 - Transformers
Chapter 13 - Graph neural networks
Chapter 14 - Unsupervised learning
Chapter 15 - Generative adversarial networks
Chapter 16 - Normalizing flows
Chapter 17 - Variational autoencoders
Chapter 18 - Diffusion models
Chapter 19 - Deep reinforcement learning
Chapter 20 - Why does deep learning work?
Chapter 21 - Deep learning and ethics
chapter, Networks, Learning, Deep
