ML Lecture 0-1 Introduction of Machine Learning

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2018-06-23 12:55:48
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一名热爱数学的IT工科男
视频选集
(35/36)
ML Lecture 0-1 Introduction of Machine Learning
38:57
ML Lecture 0-2 Why we need to learn machine learning
01:20
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
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
09:37
ML Lecture 8-3 Keras Demo
11:14
ML Lecture 9-1 Tips for Training DNN
01:26:03
ML Lecture 9-2 Keras Demo 2
15:21
ML Lecture 9-3 Fizz Buzz in Tensorflow (sequel)
06:11
ML Lecture 10 Convolutional Neural Network
01:19:29
ML Lecture 11 Why Deep
57:45
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 2 Linear Model
24:15
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