The topics covered in this reading course include: Chapter 1. Network structure:
Residual networks; Chapter 2. Transformers; Chapter 3. Graph neural networks; Chapter 4. Deep Generative models: GANs, Normalizing flows, and Variational autoencoders; Chapter 5. Deep Generative models: Diffusion models; Chapter 6. Deep reinforcement learning; Chapter 7. Deep learning and its application sin Science (biology, physics and chemistry). Students should seek approval from the course instructor to take this reading course.