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# System prepended metadata

title: 課程｜機器學習
tags: [課程]

---

# 機器學習
## Lecture 1:Introduction of Deep Learning
- sigmoid function:
    - sigmoid 可以近似一個水平斜線水平的 function
    - piecewise linear 可以寫成 b + 一些水平斜線水平的 function
    - 一層用 sigmoid 的 NN 模型，可以模擬 piecewise linear function
- ML ~ 尋找 function
    - 1.定義 function 和參數
    - 2.定義 loss 
    - 3.optimization
- 論文/links
    - [Prob Gen Model](https://www.youtube.com/watch?v=fZAZUYEeIMg&ab_channel=Hung-yiLee) / [Logistic Regression](https://www.youtube.com/watch?v=hSXFuypLukA&ab_channel=Hung-yiLee)
    - [restricted Boltzmann machine](https://zh.wikipedia.org/wiki/%E5%8F%97%E9%99%90%E7%8E%BB%E5%B0%94%E5%85%B9%E6%9B%BC%E6%9C%BA)
    - [arxiv1811: Activation Functions: Comparison of Trends in Practice and Research for Deep Learning](https://arxiv.org/pdf/1811.03378.pdf)
## Lecture 2:What to do if my network fails to train
- 訓練的情境
    - 訓練上表現不好 -> 可以由淺入深增加模型彈性來判斷
        - Model Bias: 模型不夠 powerful
        - Optimization: gd 還沒找到足夠好的參數
    - 訓練表現好但測試不好 ->
        - Overfit: 訓練表現變好但測試表現變差
            - 越 powerful 模型越容易 overfit
            - 增加資料量
            - 限制模型 / early stop / regularization / dropout
        - Mismatch: 訓練資料和測試資料分布相異
- Optimization 的技巧
    - 判斷是否 saddle point / local minimum
    - batch 更新參數
    - Momentum
    - Adaptive learning rate
- 分類問題的數學分析
    - 
## Lecture 3:Image as input
## Lecture 4:Sequence as input	
## Lecture 5:Sequence to sequence
## Lecture 6:Generation
## Lecture 7:Self-supervied Learning
## Lecture 8:Auto-encoder / Anomaly Detection
## Lecture 9:Explainable AI
## Lecture 10:Attack
## Lecture 11:Adaptation	
## Lecture 12:Reinforcement Learning
## Lecture 13:Network Compression
## Lecture 14:Life-long Learning
## Lecture 15:Meta Learning
