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Bayesian Deep Learning

최성준

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Bayesian Deep Learning

Bayesian Deep Learning

http://www.boostcourse.org/bayesiandeeplearning

좋아요 621 수강생 5965

하위 메뉴

강의목록
1. Introduction
2. Set theory
3. Measure theory
4. Probability
5. Random variable
6. Random process
7. Functional analysis
1. Introduction
2. Gaussian process
3. Weight space view
4. Function space view
5. Gaussian process latent variable model (GPLVM)
6. Gaussian process Application : 최성준님 연구 소개
1. Introduction
2. Minimizing the Description Length
3. Ensemble Learning in Bayesian Neural Network
4. Practical variational inference
5. Bayes by backprop (BBB)
1. Summary of Variational Inference
2. Dropout as a Bayesian Approximation
3. Stein Variational Gradient Descent
1. Summary
공지게시판
Discussion Forum

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강의목록 도움말

CHAPTER 1. Elementary of mathematics

강의시간02:06:58
CHAPTER 2. Gaussian process

강의시간01:39:48
CHAPTER 3. Bayesian deep neural network (1)

강의시간01:16:22
CHAPTER 4. Bayesian deep neural network (2)

강의시간01:28:13
CHAPTER 5. Summary

강의시간45:53
- Summary
  
  작성자
  
  업데이트 :
  
  2025.06.16
  
  좋아요
  
  10
  
  동영상
  
  강의시간45:53
CHAPTER 6. Uncertainties in Deep Learning

강의시간01:34:26

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