2019w_deeplearning_seminar

2019.1~2 deep learning seminar

jupyter notebook을 이용하여 ipynb파일로 업로드 또는 ppt자료를 pdf로 변환하여 업로드

발표 일정

Chapter	Presenter	Date
13 Linear Factor Model	박현근	1/3
14 Autoencoder	김동현	1/10
15 Representation Learning	권선우	1/17
(+) supervised learning abstract	김양곤	1/17
16 Structured Probabilistic Models for Deep Learning	구범혁	1/24
17 Monte Carlo Methods	안재영	1/31
(+) unsupervised learning abstract	김양곤	1/31
20 Deep Generative Models	김진범	2/14
.	.	2/21
.	.	2/28

Name		Name	Last commit message	Last commit date
Latest commit History 47 Commits
Chapter 13. Linear Factor Models.ipynb		Chapter 13. Linear Factor Models.ipynb
Chapter 13. Probabilistic Graph Model.pdf		Chapter 13. Probabilistic Graph Model.pdf
Chapter 14. Autoencoder.pdf		Chapter 14. Autoencoder.pdf
Chapter 15. Representation Learning.pdf		Chapter 15. Representation Learning.pdf
Chapter 16. Probabilistic Graph Model.pdf		Chapter 16. Probabilistic Graph Model.pdf
Chapter 17. Monte Carlo Methods.pdf.pdf		Chapter 17. Monte Carlo Methods.pdf.pdf
Chapter 20. Deep Generative Models.pdf		Chapter 20. Deep Generative Models.pdf
README.md		README.md
Yanggon_Supervised_Learning.pdf		Yanggon_Supervised_Learning.pdf
Yanggon_Unsupervised_Learning.pdf		Yanggon_Unsupervised_Learning.pdf
Yanggon_eigenface.pdf		Yanggon_eigenface.pdf