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DeepBayes2018：深度学习贝叶斯6天速成课程

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2018-09-10 03:52:13

项目地址：https://github.com/bayesgroup/deepbayes-2018 视频地址：https://www.youtube.com/playlist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62 PPT 地址：https://drive.google.com/drive/folders/1rJ-HTN3sNTvhJXPoXEEhfGlZWtjNY26C 应大家的要求大部分视频已上传油管机翻中英文字幕，个别视频没有字幕请大家见谅

一只不会划水的鹅发消息

[DeepBayes2018] Day 1, practical session 2. Bayesian reasoning

28:11

[DeepBayes2018] Day 1, lecture 3. Models with latent variables and EM-algorithm

01:31:59

[DeepBayes2018] Day 1, practical session 4. EM-algorithm (part 1)

43:32

[DeepBayes2018] Day 1, practical session 5. EM-algorithm (part 2)

07:32

[DeepBayes2018] Day 2, lecture 1. Introduction to stochastic optimization

01:32:20

[DeepBayes2018] Day 2, lecture 2. Scalable Bayesian methods

01:02:03

[DeepBayes2018] Day 2, lecture 3. Variational autoencoders

01:31:32

[DeepBayes2018] Day 2, lecture 4. Discrete latent variables

01:15:51

[DeepBayes2018] Day 2, practical session 5. Variational autoencoders

21:51

[DeepBayes2018] Day 3, Invited talk 1. Advanced methods of variational inference

01:41:54

[DeepBayes2018] Day 3, IT 2. Reinforcement learning through the lense of variati

01:05:49

[DeepBayes2018] Day 3, Practical session 3. Reinforcement learning

59:27

[DeepBayes2018] Day 3, lecture 4. Distributional reinforcement learning

01:02:19

[DeepBayes2018] Day 3, Practical session 5. Distributional reinforcement learnin

27:38

[DeepBayes2018] Day 4, lecture 1. Generative models

01:13:14

[DeepBayes2018] Day 4, Practical session 2. Adversarial learning

12:44

[DeepBayes2018] Day 4, Invited talk 3. Extending the Reparameterization Trick

01:25:08

[DeepBayes2018] Day 4, Sponsor talk 4. Yaroslav LabutinRymsho. Samsung Research

31:35

[DeepBayes2018] Day 5, Lecture 1. Gaussian processes

01:28:01

[DeepBayes2018] Day 5, Practical session 2. Bayesian optimization

01:22:19

[DeepBayes2018] Day 5, Invited talk 3. Deep Gaussian processes

01:35:43

[DeepBayes2018] Day 5, lecture 4. Markov chain Monte Carlo

56:20

[DeepBayes2018] Day 5, Lecture 5. Stochastic Markov chain Monte Carlo

01:08:29

[DeepBayes2018] Day 6, Lecture 1. Bayesian neural networks and variational dropo

01:21:06

[DeepBayes2018] Day 6, Lecture 2. Sparse variational dropout and variance networ

01:08:39

[DeepBayes2018] Day 6, Practical session 3. Neural networks sparsification

10:22

[DeepBayes2018] Day 6, Invited talk 4. Information bottleneck

01:18:28

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