下载方式，后台回复：理解深度学习

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 auto-encoders

  • Chapter 18 - Diffusion models

  • Chapter 19 - Deep reinforcement learning

  • Chapter 20 - Why does deep learning work?