https://github.com/acecoooool/mlds-note
:books: This is note for Machine Learning and having it deep and structrured (Hung-yi Lee)
https://github.com/acecoooool/mlds-note
course deep-learning machine-learning python pytorch tutorial
Last synced: 5 months ago
JSON representation
:books: This is note for Machine Learning and having it deep and structrured (Hung-yi Lee)
- Host: GitHub
- URL: https://github.com/acecoooool/mlds-note
- Owner: AceCoooool
- Created: 2018-08-11T13:35:30.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-09-04T15:33:19.000Z (over 7 years ago)
- Last Synced: 2025-04-21T09:52:05.288Z (9 months ago)
- Topics: course, deep-learning, machine-learning, python, pytorch, tutorial
- Language: Jupyter Notebook
- Homepage:
- Size: 39.8 MB
- Stars: 13
- Watchers: 2
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# MLDS-Note
关于[Machine Learning and having it deep and structured (2018,Spring)](http://speech.ee.ntu.edu.tw/~tlkagk/courses_MLDS18.html)课程的学习笔记
| hw1 | hw3 |
| :--: | :----------------------------------------------------------: |
|
| 
|
| hw2 | hw4 |
| | |
## 目录
### [chapter 1: Why Deep Structure](ch1/ch1.md)
- [1-1 Can shallow network fit any function](ch1/ch1_1.md)
- [1-2 Potential of Deep](ch1/ch1_2.md)
- [1-3 Is Deep better than Shallow](ch1/ch1_3.md)
### [chapter 2: Optimization](ch2/ch2.md)
- [2-1 When Gradient is Zero](ch2/ch2_1.md)
- [2-2 Deep Linear Network](ch2/ch2_2.md)
- [2-3 Does Deep Network have Local Minima](ch2/ch2_3.md)
- [2-4 Geometry of Loss Surfaces (Conjecture)](ch2/ch2_4.md)
- [2-5 Geometry of Loss Surfaces (Empirical)](ch2/ch2_5.md)
### [chapter 3: Generalization](ch3/ch3.md)
- [3-1 Capability of Generalization](ch3/ch3_1.md)
- [3-2 Indicator of Generalization](ch3/ch3_2.md)
### [chapter 4: Computational Graph](ch4/ch4.md)（coming soon）
### [chapter 5: Special Network Structure](ch5/ch5.md)
- [5-1 RNN with Gated Mechanism](ch5/ch5_1.md)
- [5-2 Sequence Generation](ch5/ch5_2.md)
- [5-3 Conditional Sequence Generation](ch5/ch5_3.md)
- [5-4 Tips for Generation](ch5/ch5_4.md)
- [5-5 Pointer Network](ch5/ch5_5.md)
- [5-6 Recursive Structure](ch5/ch5_6.md)
- [5-7 Attention-based Model](ch5/ch5_7.md)
### [chapter 6: Special Training Technology](ch6/ch6.md)
- [6-1 Tips for Training Deep Network](ch6/ch6_1.md)
- [6-2 Automatically Determining Hyperparameters](ch6/ch6_2.md)
### [chapter 7: Generative Adversarial Network (GAN)](ch7/ch7.md)
- [7-1 Introduction of Generative Adversarial Network (GAN)](ch7/ch7_1.md)
- [7-2 Conditional Generation by GAN](ch7/ch7_2.md)
- [7-3 Unsupervised Conditional Generation](ch7/ch7_3.md)
- [7-4 Theory behind GAN](ch7/ch7_4.md)
- [7-5 fGAN：General Framework of GAN](ch7/ch7_5.md)
- [7-6 Tips for Improving GAN](ch7/ch7_6.md)
- [7-7 Feature Extraction](ch7/ch7_7.md)
- [7-8 Intelligent Photo Editing](ch7/ch7_8.md)
- [7-9 Application to Sequence Generation](ch7/ch7_9.md)（coming soon）
- [7-10 Evaluation](ch7/ch7_10.md)
### [chapter 8: Deep Reinforcement Learning](ch8/ch8.md)
- [8-1 Introduction of Reinforcement Learning](ch8/ch8_1.md)
- [8-2 Policy-based Approach (Learning an Actor)](ch8/ch8_2.md)
- [8-3 Proximal Policy Optimization (PPO)](ch8/ch8_3.md)
- [8-4 Q-Learning (1)](ch8/ch8_4.md)
- [8-5 Q-Learning (2)](ch8/ch8_5.md)
- [8-6 Actor-Critic](ch8/ch8_6.md)
- [8-7 Sparse Reward](ch8/ch8_7.md)
- [8-8 Imitation Learning](ch8/ch8_8.md)
### [Appendix: homework](homework/README.md)
- [hw1](homework/README.md)：finish
- [hw2](homework/README.md)：TODO（not recently）
- [hw3](homework/README.md)：coming soon
- [hw4](homework/README.md)：coming soon
## 说明
1. 上述内容均来自于[MLDS](http://speech.ee.ntu.edu.tw/~tlkagk/courses_MLDS18.html)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）
2. 可以clone到本地，再用其他markdown阅读工具（个人采用typora编辑）
3. 请勿用于其他商业用途