[Note] Machine Learning (Lecture 6)

Deep Learning

I believe you have seen lots of exciting results before.
ex : Android, Apps, drug discovery, Gmail, Image understanding…

Neural Network:
有 Input Layer, 多個 Hidden Layer, Output Layer。

其中包括數個 weight 以及 bias 來計算 Neural Network 每層參數輸出,
也可以看作一系列的矩陣運算,代表可以透過 GPU 加速。

Example: 數字比對

  1. 透過 Neural Network 建立 Model
    Input Layer: x1 ~ xn 的 node 建立 input layer
    Hidden Layers: fully connected network training structure
    Output Layer: y1 ~ y10 輸出數字為 0 ~ 9 之數字機率
  2. 藉由 Gradient Descent 來改正 model parameter,
    藉此降低 Loss function

3 個問題

  1. 如何決定 Neural Network 層數 ?
    A: 藉由測試跟經驗決定
  2. 如何自動決定 Network Structure ?
    A: Evolutionary Artifical Neural Networks
  3. 怎麼去設計 Network Structure ?
    A: Convolutional Neural Network (CNN)

圖片文章來源 :

Lecturer

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Major <computer science>

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Jackson Chen

Jackson Chen

Major <computer science>

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