Weidong "Will" Kuang

Table of Contents

• Preface xv
• Mathematical Notation xxi
• Chapter 1: Introduction to Deep Learning 1
• Chapter 2: Linear Regression 19
• Chapter 3: Classification and Logistic Regression 45
• Chapter 4: Basics of Neural Networks 67
• Chapter 5: Practical Considerations in Neural Networks 107
• Chapter 6: Introduction to PyTorch 171
• Chapter 7: Convolutional Neural Networks 205
• Chapter 8: Classic Architectures of CNNs 239
• Chapter 9: Object Detection - YOLO 269
• Chapter 10: Introduction to Probabilistic Generative Models 319
• Chapter 11: Generative Adversarial Networks 351
• Chapter 12: Diffusion Models 399
• Chapter 13: Word Embedding 439
• Chapter 14: Recurrent Neural Networks 475
• Chapter 15: Transformer 509
• Chapter 16: Introduction to Reinforcement Learning 547
• Chapter 17: Deep Q-Learning 579
• Chapter 18: Policy Gradient Methods 601
• Appendix A 657
• Index 717