[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 :





Major <computer science>

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Credit Card Fraud Detection

Lazy Predict: Running Many Machine Learning Models Quick and Simply

Azure Video Analyzer for Media — Quick Notes

Robotic Grasp Detection — Resnet, EfficientNet

TensorFlow and Keras. A Beginners Tutorial by a Beginner

Neural network on Python

Tune your Channel Attribution Model

Collaborative filtering on encoded disease data.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Jackson Chen

Jackson Chen

Major <computer science>

More from Medium

Implementing Naive Bayes From Scratch in Python

Iris Dataset, But Make It Interesting!

Python is the language of choice for data scientists

Why do data scientists waste up to 70% of their time and money collecting and cleaning data?