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==introduction==
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* Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems.
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== articles ==
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* Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami, Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks, http://arxiv.org/abs/1511.04508v2
 
* https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner
 
* https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner
 
* https://brunch.co.kr/@justinleeanac/2
 
* https://brunch.co.kr/@justinleeanac/2
 
* http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html
 
* http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html
 
* Silver, David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, et al. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature 529, no. 7587 (January 28, 2016): 484–89. doi:10.1038/nature16961.
 
* Silver, David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, et al. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature 529, no. 7587 (January 28, 2016): 484–89. doi:10.1038/nature16961.
 
== articles ==
 
 
* Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami, Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks, http://arxiv.org/abs/1511.04508v2
 

2016년 3월 16일 (수) 00:05 판

introduction

  • Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems.


articles