视频选集
（29/38）
ML Lecture 0 - Introduction of Machine Learning
38:57
ML Lecture 1 - Regression - Case Study
01:18:35
ML Lecture 1 - Regression - Demo
06:53
ML Lecture 2 - Where does the error come from
43:14
ML Lecture 3-1 - Gradient Descent
01:01:52
ML Lecture 3-2 - Gradient Descent (Demo by AOE)
02:36
ML Lecture 3-3 - Gradient Descent (Demo by Minecraft)
01:41
ML Lecture 4 - Classification
01:09:41
ML Lecture 5 - Logistic Regression
01:07:13
ML Lecture 6 - Brief Introduction of Deep Learning-Dr
46:30
ML Lecture 7 - Backpropagation
31:26
ML Lecture 8-1 - “Hello world” of deep learning
29:51
ML Lecture 8-2 - Keras 2.0
09:38
ML Lecture 8-3 - Keras Demo
11:14
ML Lecture 9-1 - Tips for Training DNN
01:26:02
ML Lecture 9-2 - Keras Demo 2
15:21
ML Lecture 9-3 - Fizz Buzz in Tensorflow (sequel)
06:10
ML Lecture 10 - Convolutional Neural Network
01:19:29
ML Lecture 11 - Why Deep
57:46
ML Lecture 12 - Semi-supervised
59:59
ML Lecture 13 - Unsupervised Learning - Linear Methods
01:40:20
ML Lecture 14 - Unsupervised Learning - Word Embedding
40:39
ML Lecture 15 - Unsupervised Learning - Neighbor Embedding
30:58
ML Lecture 16 - Unsupervised Learning - Auto-encoder
42:04
ML Lecture 17 - Unsupervised Learning - Deep Generative Model (Part I)
29:34
ML Lecture 18 - Unsupervised Learning - Deep Generative Model (Part II)
01:03:31
ML Lecture 19 - Transfer Learning
01:14:28
ML Lecture 20 - Support Vector Machine (SVM)
01:05:27
ML Lecture 21-1 - Recurrent Neural Network (Part I)
49:00
ML Lecture 21-2 - Recurrent Neural Network (Part II)
01:30:50
ML Lecture 22 - Ensemble
01:39:59
ML Lecture 23-1 - Deep Reinforcement Learning
01:06:22
ML Lecture 23-2 - Policy Gradient (Supplementary Explanation)
13:20
ML Lecture 23-3 - Reinforcement Learning (including Q-learning)
01:05:34
Structured Learning 1 - Introduction
19:33
Structured Learning 2 - Linear Model
24:15
Structured Learning 3 - Structured SVM
02:11:45
Structured Learning 4 - Sequence Labeling
01:43:31
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