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李宏毅机器学习(2017)

94.1万

2.4万

2017-05-16 00:23:09

src: https://www.youtube.com/playlist?list=PLJV_el3uVTsPy9oCRY30oBPNLCo89yu49 Home: http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML17.html

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0-1: Introduction of Machine Learning

38:57

0-2: Why we need to learn machine learning?

01:20

1: Regression - Case Study

01:25:57

1- Regression - Demo - YouTube (HD)

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)

02:35

3-3- Gradient Descent (Demo by Minecraft)

01:40

HW1 – PM2.5 Prediction

14:06

4: Classification

01:09:41

5: Logistic Regression

01:09:09

HW2 - Winner or Loser

03:21

6: Brief Introduction of Deep Learning

46:32

7: Backpropagation

20:20

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

01:22:35

9-2- Keras Demo 2

15:21

9-3- Fizz Buzz in Tensorflow (sequel)

06:10

10: Convolutional Neural Network

01:23:26

11: Why Deep?

57:46

12: Semi-supervised

01:00:00

13: Unsupervised Learning - Linear Methods

01:40:24

14: Unsupervised Learning - Word Embedding

42:10

15: Unsupervised Learning - Neighbor Embedding

30:29

16: Unsupervised Learning - Auto-encoder

36:52

17- Unsupervised Learning - Deep Generative Model (Part I)

29:34

18- Unsupervised Learning - Deep Generative Model (Part II)

01:03:31

19: Transfer Learning

01:11:26

20- Support Vector Machine (SVM)

01:05:26

21- Structured Learning - Introduction

19:32

22- Structured Learning - Linear Model

24:15

23- Structured Learning - Structured SVM

02:11:44

24- Structured Learning - Sequence Labeling

01:43:31

25: Recurrent Neural Network (Part I)

49:01

26: Recurrent Neural Network (Part II)

01:30:56

27: Ensemble

01:40:02

28- Deep Reinforcement Learning - Scratching the surface

01:06:21

The Next Step for Machine Learning

15:27

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