This reading course covers the following topics: 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, Diffusion models Chapter 5. Large language models and its application Chapter 6. Deep learning and its applications in Science. Students should seek approval from the course instructor to take this reading course.