~ read.

Resources

arXiv daily paper
also can see https://www.52ml.net

ICML:https://icml.cc/
NIPS:https://nips.cc/
ICLR:https://iclr.cc/

ICCV:http://iccv2017.thecvf.com/
CVPR:http://cvpr2017.thecvf.com/

An interesting blog:https://handong1587.github.io/

Spotlight/Oral/Tutorial in ICCV2017
https://www.youtube.com/channel/UC0n76gicaarsN_Y9YShWwhw/playlists

Deep Learning: Theory, Algorithms, and Applications. Berlin, June 2017
https://www.youtube.com/playlist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW

Stanford Stat385 Theory Of Deep Learning Course
https://stats385.github.io/

GAN materials:
https://github.com/nightrome/really-awesome-gan
https://github.com/rockt/ganhacks

ICML2017 Theory Deep Learning workshop:
http://padl.ws/

Bayesian Deep Learning Workshop(NIPS2017):
http://bayesiandeeplearning.org/

PAC Bayesian Workshop:
https://bguedj.github.io/nips2017/50shadesbayesian.html

NIPS2017 Deep learning workshop:
https://ludwigschmidt.github.io/nips17-dl-workshop-website/

NIPS2017 Deep Learning For physics workshop:
https://dl4physicalsciences.github.io/

NIPS2017 Learning on Distributions, Functions, Graphs and Groups Workshop
https://sites.google.com/site/nips2017learningon/

NIPS2017 Optimal Transport And Machine Learning Workshop:
http://otml17.marcocuturi.net/

NIPS2017 Synergies in Geometric Data Analysis (TWO DAYS)
https://nips.cc/Conferences/2017/Schedule?showEvent=8786