Deep Learning Resources

Deep Learning Frameworks

  • Caffe – Deep learning framework developed by Yangqing Jia while in the PhD program at University of California at Berkeley
  • Torch - A scientific computing framework with wide support for machine learning algorithms
  • Theano - A Python library that allows you to efficiently define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays

Deep Learning Courses

  • Andrew Ng's Coursera course provides a good introduction to deep learning (Coursera, YouTube)
  • Yann LeCun’s NYU Course on Deep Learning, Spring 2014 (TechTalks)
  • Geoffrey Hinton's “Neural Networks for Machine Learning” course from Oct 2012 (Coursera)
  • Rob Fergus's "Deep Learning for Computer Vision" tutorial from NIPS 2013 (slides, video).
  • Caltech's introductory deep learning course taught by Yasser Abu-Mostafa (YouTube)
  • Stanford CS224d: Deep Learning for Natural Language Processing (video, slides, tutorials)

Deep Learning Talks

  1. Andrew Ng from Stanford/Google. Machine Learning & AI Via Large Scale Brain Simulation
  2. Yann LeCun from NYU/Facebook. Computer Perception with Deep Learning

Deep Learning Papers

  • Nature: Deep learning(LeCun, Bengio, Hinton) [PDF]
  • Nature: Reinforcement learning improves behaviour from evaluative feedback (Littman) [PDF]
  • Sequence to Sequence Learning with Neural Networks (Sutskever (Google), NIPS 2015) [video, paper]
  • Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Christian Szegedy, Sergey Ioffe. (2015) [PDF]
  • Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. (2015) [PDF]
  • Deep Image: Scaling up Image Recognition, Ren Wu, Shengen Yan, Yi Shan, Qingqing Dang, Gang Sun. (2015) [PDF]
  • cuDNN: Efficient Primitives for Deep Learning, Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer. (2014). [PDF]
  • One weird trick for parallelizing convolutional neural networks, Alex Krizhevsky. (2014) [PDF]
  • Multi-GPU Training of ConvNets, Omry Yadan, Keith Adams, Yaniv Taigman, Marc’Aurelio Ranzato. (2014) [PDF]
  • Deep Learning with COTS HPC, Adam Coates, Brody Huval, Tao Wang, David J. Wu, Andrew Y. Ng, Bryan Catanzaro. (2013) [PDF]
  • OverFeat: Integrated Recognition, Localization, and Detection using Convolutional Networks, Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann LeCun. (2013) [PDF]
  • ImageNet Classification with Deep Convolutional Neural Networks, Krizhevsky, A., Sutskever, I. Hinton, G. E. (2012) [PDF]
  • Large Scale Distributed Deep Networks, Jeffrey Dean, Greg S. Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc’Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, Andrew Y. Ng. (2012) [PDF]

Deep Learning Blog Posts:


Parallel Forall Blog: