MLDS-Note
关于Machine Learning and having it deep and structured (2018,Spring)课程的学习笔记
| hw1 | hw3 |
|---|---|
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| hw2 | hw4 |
目录
chapter 1: Why Deep Structure
chapter 2: Optimization
- 2-1 When Gradient is Zero
- 2-2 Deep Linear Network
- 2-3 Does Deep Network have Local Minima
- 2-4 Geometry of Loss Surfaces (Conjecture)
- 2-5 Geometry of Loss Surfaces (Empirical)
chapter 3: Generalization
chapter 4: Computational Graph（coming soon）
chapter 5: Special Network Structure
- 5-1 RNN with Gated Mechanism
- 5-2 Sequence Generation
- 5-3 Conditional Sequence Generation
- 5-4 Tips for Generation
- 5-5 Pointer Network
- 5-6 Recursive Structure
- 5-7 Attention-based Model
chapter 6: Special Training Technology
chapter 7: Generative Adversarial Network (GAN)
- 7-1 Introduction of Generative Adversarial Network (GAN)
- 7-2 Conditional Generation by GAN
- 7-3 Unsupervised Conditional Generation
- 7-4 Theory behind GAN
- 7-5 fGAN：General Framework of GAN
- 7-6 Tips for Improving GAN
- 7-7 Feature Extraction
- 7-8 Intelligent Photo Editing
- 7-9 Application to Sequence Generation（coming soon）
- 7-10 Evaluation
chapter 8: Deep Reinforcement Learning
- 8-1 Introduction of Reinforcement Learning
- 8-2 Policy-based Approach (Learning an Actor)
- 8-3 Proximal Policy Optimization (PPO)
- 8-4 Q-Learning (1)
- 8-5 Q-Learning (2)
- 8-6 Actor-Critic
- 8-7 Sparse Reward
- 8-8 Imitation Learning
Appendix: homework
说明
- 上述内容均来自于MLDS课程，欢迎交流与讨论（可能里面会有我个人理解错误的地方，欢迎指出）
- 可以clone到本地，再用其他markdown阅读工具（个人采用typora编辑）
- 请勿用于其他商业用途