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