Machine Learning (李宏毅,2017)

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2019-06-02 01:03:01
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https://www.youtube.com/watch?v=CXgbekl66jc&list=PLJV_el3uVTsPy9oCRY30oBPNLCo89yu49 本系列教程均转载自李宏毅老师上传至youtube上的视频,转载已取得老师同意。 视频源地址:https://www.youtube.com/watch?v=CXgbekl66jc&list=PLJV_el3uVTsPy9oCRY30oBPNLCo89yu49
视频选集
(18/35)
0-1_ Introduction of Machine Learning
38:57
0-2_ Why we need to learn machine learning?
01:20
1_ Regression - Case Study
01:18:35
1_ Regression - Demo
06:53
2_ Where does the error come from?
43:14
3-1_ Gradient Descent
01:01:52
3-2_ Gradient Descent (Demo by AOE)
02:36
3-3_ Gradient Descent (Demo by Minecraft)
01:41
4_ Classification
01:09:41
5_ Logistic Regression
01:07:13
6_ Brief Introduction of Deep Learning
46:30
7_ Backpropagation
31:26
8-1_ “Hello world” of deep learning
29:51
8-2_ Keras 2.0
09:37
8-3_ Keras Demo
11:13
9-1_ Tips for Training DNN
01:26:02
9-2_ Keras Demo 2
15:21
9-3_ Fizz Buzz in Tensorflow (sequel)
06:10
10_ Convolutional Neural Network
01:19:29
11_ Why Deep
57:45
12_ Semi-supervised
59:59
13_ Unsupervised Learning - Linear Methods
01:40:20
14_ Unsupervised Learning - Word Embedding
40:39
15_ Unsupervised Learning - Neighbor Embedding
30:58
16_ Unsupervised Learning - Auto-encoder
42:04
17_ Unsupervised Learning - Deep Generative Model (Part I)
29:34
18_ Unsupervised Learning - Deep Generative Model (Part II)
01:03:31
19_ Transfer Learning
01:14:28
20_ Support Vector Machine (SVM)
01:05:27
21-1_ Recurrent Neural Network (Part I)
49:00
21-2_ Recurrent Neural Network (Part II)
01:30:50
22_ Ensemble
01:39:59
23-1_ Deep Reinforcement Learning
01:06:22
23-2_ Policy Gradient (Supplementary Explanation)
13:20
ML Lecture 23-3_ Reinforcement Learning (including Q-learning)
01:05:34
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