“深度学习-入门导读”版本间的差异

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=入门导读=
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=== Stanford ===
  
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深度学习教程。
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# Stanford Deep Learning tutorials [http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial DL_tutorials]
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# Tutorials by Andrew Ng [http://deeplearning.stanford.edu/tutorial/ DL11-2015] [http://ufldl.stanford.edu/tutorial/ DL22-2015]
  
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==斯坦福==
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=== Google Brain ===
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# Andrew Ng实验室 [http://deeplearning.stanford.edu/tutorial/ DL11-2015] [http://ufldl.stanford.edu/tutorial/ DL22-2015]
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# Stanford Deep Learning tutorials [http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial DL_tutorials]
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# Jeffrey Dean et al. "Large scale distributed deep networks." NIPS 2012. [https://research.google.com/pubs/pub40565.html Abstract] [https://research.google.com/pubs/archive/40565.pdf L-BFGS_Paper]
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# Jeff Dean, Large-Scale Deep Learning for Intelligent Computer Systems, WSDM 2016. [https://research.google.com/pubs/archive/44921.pdf WSDM_keynote]
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# TensorFlow: A System for Large-Scale Machine Learning, OSDI 2016. [http://research.google.com/pubs/pub45381.html Abstract] [https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf TensorFlow_OSDI2016_Paper]
  
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==谷歌==
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=== Nature/Science ===
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Jeff Dean谷歌大脑
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# Jeff Dean, Large-Scale Deep Learning for Intelligent Computer Systems, WSDM 2016. [http://research.google.com/pubs/jeff.html WSDM_keynote]
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# D. E. Rumelhart, G. E. Hinton, and R. J. Williams. "Learning representations by back-propagating errors." Nature 323.6088:533-536, 1986. [http://www.nature.com/nature/journal/v323/n6088/abs/323533a0.html SGD]
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# Jeffrey Dean et al. "Large scale distributed deep networks." Advances in Neural Information Processing Systems. 2012.
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# Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521(7553), pp:436-444, 2015. [http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html Deep_Learning_Nature]
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# TensorFlow: A System for Large-Scale Machine Learning, OSDI 2016. [https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf TensorFlow_OSDI2016_paper] [http://research.google.com/pubs/pub45381.html TensorFlow_paper]
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==Nature/Science==
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=== 书籍 ===
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# DAVID E. RUMELHART, GEOFFREY E. HINTON & RONALD J. WILLIAMS, Learning representations by back-propagating errors, Nature 323, 533 - 536,09 October 1986. [http://www.nature.com/nature/journal/v323/n6088/abs/323533a0.html SGD]
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# LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521(7553), pp:436-444, 2015. [http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html Deep_Learning_Nature]
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==书籍==
 
 
# Yoshua Bengio, Ian Goodfellow, Aaron Courville, Deep Learning, MIT Press, 2016. [http://www.deeplearningbook.org/ DeepLearningBook]
 
# Yoshua Bengio, Ian Goodfellow, Aaron Courville, Deep Learning, MIT Press, 2016. [http://www.deeplearningbook.org/ DeepLearningBook]
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# Yu, Dong, and Li Deng. Automatic speech recognition: A deep learning approach. Springer, 2014.
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# Deng, Li, and Dong Yu. "Deep learning: methods and applications." Foundations and Trends® in Signal Processing 7, no. 3–4 (2014): 197-387.

2017年11月23日 (四) 01:55的最后版本

Stanford

  1. Stanford Deep Learning tutorials DL_tutorials
  2. Tutorials by Andrew Ng DL11-2015 DL22-2015

Google Brain

  1. Jeffrey Dean et al. "Large scale distributed deep networks." NIPS 2012. Abstract L-BFGS_Paper
  2. Jeff Dean, Large-Scale Deep Learning for Intelligent Computer Systems, WSDM 2016. WSDM_keynote
  3. TensorFlow: A System for Large-Scale Machine Learning, OSDI 2016. Abstract TensorFlow_OSDI2016_Paper

Nature/Science

  1. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. "Learning representations by back-propagating errors." Nature 323.6088:533-536, 1986. SGD
  2. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521(7553), pp:436-444, 2015. Deep_Learning_Nature

书籍

  1. Yoshua Bengio, Ian Goodfellow, Aaron Courville, Deep Learning, MIT Press, 2016. DeepLearningBook
  2. Yu, Dong, and Li Deng. Automatic speech recognition: A deep learning approach. Springer, 2014.
  3. Deng, Li, and Dong Yu. "Deep learning: methods and applications." Foundations and Trends® in Signal Processing 7, no. 3–4 (2014): 197-387.
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