Machine_Learning_Tutorial

This tutorial covers some Machine Learning basics with video presentations and code implementations.

Usage Guidebook

1. Open the file in Github
2. Click the button said "Open in Colab"
3. Launch Google Colab and start working!

Tutorial Materials

1. Machine Learning basic concepts

2. Gradient Descent & Linear Regression
   2.1 (8 mins) Watch [Cost Function - Andrew Ng] (https://www.bilibili.com/video/BV1AD4y1Q7RH?p=5)
   2.2 (11 mins) Watch [Gradient Descent - Andrew Ng] (https://www.bilibili.com/video/BV1AD4y1Q7RH?p=5)
   2.3 (12 mins) Watch Gradient Descent Intuition - Andrew Ng
   2.4 (10 mins) Watch Gradient Descent for Linear Regression - Andrew Ng
   2.5 (19 mins) Watch But what is a Neural Network? | Deep learning, Part 1
   2.6 (Optional, 10 mins, 偏数学推导) [Why the gradient is the direction of steepest ascent - Khan Academy] (https://www.bilibili.com/video/BV1iE411K7qv)
   

3. Gradient Descent, Forward&BackPropogation
   3.1 (21 mins with 1.25x) Watch video Gradient Descent - 3Blue1Brown
   3.2 (14 mins with 1.25x) Watch video Feedforward propagation - 3Blue1Brown
   3.3 (10 mins with 1.25x) Watch video Backpropagation - 3Blue1Brown
   3.4 (Optional) Watch video for better understanding Backpropagation 1 - Andrew Ng
   3.5 (Optional) Watch video for better understanding Backpropagation 2 - Andrew Ng
   3.6 (Optional) Read Feedforward propagation&Backpropagation 深度学习数学基础
   3.7 (10 mins) Compile Linear Regression to understand how to update weight with Pytorch and have visulization. More details about Pytorch will be introduced in Module 3
   Code Implementation: Forward/Backward Propagation
   This notebook illustrates the Forward/Backward Propagation using CBOW(Continuous bag-of-words) model, which is a word embedding model learns to predict the center word given some context words.

4. Logistic regression
   Code Implementation: Logistic regression