머신러닝/딥러닝 링크 #1

머신러닝/딥러닝 공부를 링크 모임

* 딥러닝 데모 

https://github.com/alexjc/neural-doodle

http://richzhang.github.io/colorization/

https://www.dropbox.com/s/sa8m3y1ymj0ihct/presentation_eccv_release.pptx?dl=0

https://github.com/satoshiiizuka/siggraph2016_colorization

만화 이미지 확대

http://waifu2x.udp.jp/

* 제프딘 발표 자료

http://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research

Jeff Dean at AI Frontiers: Trends and Developments in Deep Learning Research from AI Frontiers

http://www.slideshare.net/hustwj/cikm-keynotenov2014

Large Scale Deep Learning Jeff Dean from Jun Wang

* 용어 이해

<한글>

http://clien.net/cs2/bbs/board.php?bo_table=lecture&wr_id=313552&sca=%5B%EC%83%9D%ED%99%9C%EC%83%81%EC%8B%9D%5D

http://slownews.kr/41461

<전문가용>

CNN : https://research.googleblog.com/2016/08/improving-inception-and-image.html

convolution layer : http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/

pooling layer : http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/

Convolutional Neural Network : https://research.googleblog.com/2016/08/improving-inception-and-image.html

Recurrent Neural Network - http://eric-yuan.me/rnn2-lstm/

ltsm의 이해 - https://medium.com/@shiyan/understanding-lstm-and-its-diagrams-37e2f46f1714#.erqeg37dd

* 데이터 사이언티스트를 위한 딥 러닝

http://www.slideshare.net/andrewgardner5811/deep-learning-for-data-scientists-dsatl-talk-alpharetta-20140108

Deep Learning for Data Scientists - Data Science ATL Meetup Presentation, 2014-01-08 from Andrew Gardner

* 딥러닝 개념

http://www.slideshare.net/0xdata/transform-your-business-with-ai-deep-learning-and-machine-learning

Transform your Business with AI, Deep Learning and Machine Learning from SriSatish Ambati

* 딥러닝 역사

http://www.andreykurenkov.com/writing/a-brief-history-of-neural-nets-and-deep-learning-part-4/

* RNN, LSTM, BPTT 용어 정리

https://deeplearning4j.org/kr/kr-lstm

* 각광받는 회사

nvidia : http://kr.nvidia.com/object/deep-learning-kr.html

주가가 작년에 비해 10배가 뜀

https://www.google.com/finance?chdnp=1&chdd=1&chds=1&chdv=1&chvs=maximized&chdeh=0&chfdeh=0&chdet=1487145848691&chddm=98923&chls=IntervalBasedLine&q=NASDAQ:NVDA&ntsp=0&ei=dQukWJCtC8Ls0gT-7avoDg

* 학습자료

https://github.com/oxford-cs-deepnlp-2017/lectures 

https://www.youtube.com/playlist?list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA

(자막 : https://github.com/aikorea/cs231n/tree/master/captions/En)

쿄세라

Andrew Ng - https://www.coursera.org/learn/machine-learning

Daphne Koller - https://www.coursera.org/learn/probabilistic-graphical-models

Geoffrey Hinton - https://www.coursera.org/learn/neural-networks

쿄세라 강의를 위한 좋은 가이드 - https://brunch.co.kr/@aidenswmo/2

* 텐서 플로우 공부를 가이드/공부 문서

https://github.com/jtoy/awesome-tensorflow

* POS tag - 어휘와 품사 정보

딥러닝에서 사용중임.

http://kkma.snu.ac.kr/documents/?doc=postag

http://ra2kstar.tistory.com/32