来自巴斯大学计算机科学教授Simon J.D. Prince撰写的《理解深度学习》新书，共有21章，从机器学习基础概念到深度学习各种模型，包括最新的Transformer和图神经网络，扩散模型，比较系统全面，值得关注。
Chapter 1 - 导论 Introduction
Chapter 2 - 监督学习 Supervised learning
Chapter 3 - 浅层神经网络 Shallow neural networks
Chapter 4 - 深度神经网络 Deep neural networks
Chapter 5 - 损失函数 Loss functions
Chapter 6 - 训练模型 Training models
Chapter 7 - 梯度与初始化 Gradients and initialization
Chapter 8 - 度量性能 Measuring performance
Chapter 9 - 正则化 Regularization
Chapter 10 - 卷积网络 Convolutional nets
Chapter 11 - 残差网络 Residual networks and BatchNorm
Chapter 12 - Transformers
Chapter 13 - 图神经网络 Graph neural networks
Chapter 14 - 无监督学习 Unsupervised learning
Chapter 15 - 生成对抗网络 Generative adversarial networks
Chapter 16 - Normalizing flows
Chapter 17- 变分自编码器 Variational auto-encoders
Chapter 18 - 扩散模型 Diffusion models
Chapter 19 - 深度强化学习 Deep reinforcement learning
Chapter 20 - 为什么深度学习有效 Why does deep learning work?
Understanding Deep Learning
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