# machine_learning_postgraduate

**Repository Path**: lundechen/machine_learning_postgraduate

## Basic Information

- **Project Name**: machine_learning_postgraduate
- **Description**: machine_learning_postgraduate
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 2
- **Created**: 2021-11-29
- **Last Updated**: 2022-06-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Deep Learning and Deep Reinforcement Learning

## Textbooks and slides

[:books: Textbooks and slides](./Slides/)

## Part 1 - Neural Network

### Videos

- [3Blue1Brown - But what is a neural network? | Chapter 1, Deep learning](https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
- [3Blue1Brown - Gradient descent, how neural networks learn | Chapter 2, Deep learning](https://www.youtube.com/watch?v=IHZwWFHWa-w&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&index=2)
- [3Blue1Brown - What is backpropagation really doing? | Chapter 3, Deep learning](https://www.youtube.com/watch?v=Ilg3gGewQ5U&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&index=3)
- [3Blue1Brown - Backpropagation calculus | Chapter 4, Deep learning](https://www.youtube.com/watch?v=tIeHLnjs5U8&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&index=4)

### Exercises

Exercises come from the Deep Learning
Course of Andrew Ng. You can find his course
on [deeplearning.ai](https://www.deeplearning.ai/) or [coursera.org](https://www.coursera.org/specializations/deep-learning).
The corresponding videos are also available on [bilibili.com](https://www.bilibili.com/video/BV164411m79z). 

### Tencent Meeting (VooV Meeting)
腾讯会议：958-9491-5777


### Online Resources 
- [animation] https://learngitbranching.js.org/
- [animation] https://towardsdatascience.com/understanding-backpropagation-abcc509ca9d0
- [wikipedia] https://en.wikipedia.org/wiki/Hadamard_product_(matrices)
- [Stanford pdf] http://cs231n.stanford.edu/slides/2016/winter1516_lecture4.pdf
- [Stanford video] https://www.youtube.com/watch?v=i94OvYb6noo&t=1s
- [gitignore] https://gitee.com/lundechen/awesome-teaching-tools


## Part 2 - Q Learning

### Videos

- https://www.youtube.com/watch?v=JgvyzIkgxF0
- https://www.youtube.com/watch?v=A5eihauRQvo
- https://www.youtube.com/watch?v=aCEvtRtNO-M

### Code

[q_learning_car_climbs_mountain.py](Part-3 Q Learning [选考]/q_learning_car_climbs_mountain.py)


## Part 3 - Deep Q Learning

- https://www.youtube.com/watch?v=93M1l_nrhpQ
- https://www.youtube.com/watch?v=79pmNdyxEGo


大家好，
针对大家的反馈，明年学弟学妹们的冬季机器学习课程安排如图所示。
会有以下调整：
1. 从第一节课就开始使用 Google Colab，避免大家装环境的问题。
2. 必考部分只有深度学习的4个视频、吴恩达的前两个练习，其他地方为选考，可以选择一半的题目进行作答。
3. 加入了 GAN & Transfer Learning 等科研前沿的元素。
4. git, tensorflow 等都是为项目做准备。

欢迎大家继续反馈，让课程持续优化。