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【李宏毅 MLDS18 国语】机器学习深度学习 GAN 深度强化学习

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2018-11-11 16:44:45

http://speech.ee.ntu.edu.tw/~tlkagk/courses_MLDS18.html 作者：李宏毅（台湾大学）时间：2018.03~2018.06 原链接：http://speech.ee.ntu.edu.tw/~tlkagk/courses_MLDS18.html 原网站有配套的PPT以及HomeWork.

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机器学习/多agent协作/深度强化学习/因果学习

Deep Learning Theory 1-1_ Can shallow network fit any function_

36:49

Deep Learning Theory 1-2_ Potential of Deep

56:09

Deep Learning Theory 1-3_ Is Deep better than Shallow_

26:18

Deep Learning Theory 2-1_ When Gradient is Zero

01:17:29

Deep Learning Theory 2-2_ Deep Linear Network

31:29

Deep Learning Theory 2-3_ Does Deep Network have Local Minima_

17:40

Deep Learning Theory 2-4_ Geometry of Loss Surfaces (Conjecture)

17:06

Deep Learning Theory 2-5_ Geometry of Loss Surfaces (Empirical)

38:46

Deep Learning Theory 3-1_ Generalization Capability of Deep Learning

25:38

Deep Learning Theory 3-2_ Indicator of Generalization

42:56

Computational Graph & Backpropagation

01:00:54

Sequence-to-sequence Learning

01:52:50

Pointer Network

13:34

Recursive Network

24:58

Attention-based Model

35:40

ForDeep

01:09:42

Tuning Hyperparameters

23:43

GAN Lecture 1 (2018)_ Introduction

01:33:15

GAN Lecture 2 (2018)_ Conditional Generation

26:19

GAN Lecture 3 (2018)_ Unsupervised Conditional Generation

38:59

GAN Lecture 4 (2018)_ Basic Theory

01:20:19

GAN Lecture 5 (2018)_ General Framework

25:04

GAN Lecture 6 (2018)_ WGAN, EBGAN

50:07

GAN Lecture 7 (2018)_ Info GAN, VAE-GAN, BiGAN

46:03

GAN Lecture 8 (2018)_ Photo Editing

22:46

GAN Lecture 9 (2018)_ Sequence Generation

01:27:24

GAN Lecture 10 (2018)_ Evaluation u0026 Concluding Remarks

30:09

DRL Lecture 1_ Policy Gradient (Review)

45:49

DRL Lecture 2_ Proximal Policy Optimization (PPO)

41:34

DRL Lecture 3_ Q-learning (Basic Idea)

49:44

DRL Lecture 4_ Q-learning (Advanced Tips)

38:31

DRL Lecture 5_ Q-learning (Continuous Action)

14:58

DRL Lecture 6_ Actor-Critic

34:16

DRL Lecture 7_ Sparse Reward

30:16

DRL Lecture 8_ Imitation Learning

34:02

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