5-Advanced Deep Learning Topics
This is Lecture 5 of the series. It is designed to push you beyond "being able to train a standard model" to the more common advanced topics. Instead of focusing on a single model, it brings together transfer learning, generative adversarial networks, and reinforcement learning in one lecture to help you build a broader map.
What This Lecture Covers
This lecture spans a wide range of topics, but they all fall under directions you will frequently encounter as deep learning extends outward.
- Transfer Learning: introduces the basic concepts of transfer learning and its typical usage in image and text scenarios.
- Generative Adversarial Networks: starts from the basic principles of GANs, then extends to common improvements and typical applications.
- Reinforcement Learning: first covers the basic framework of reinforcement learning, then transitions to deep reinforcement learning and application scenarios.
- Course Practice: through handwritten digit generation, brings the direction of "generative models" into concrete results.
How to Study This
- You do not need to master everything in this lecture at once. A more reasonable approach is to solidify your understanding of transfer learning first, as it is closest to everyday projects.
- When studying GANs, focus on understanding the adversarial relationship between the generator and discriminator, and why training can be unstable.
- When studying reinforcement learning, first distinguish basic concepts like state, action, and reward, then understand which part of the capability the deep model replaces.