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  1. debasishg revised this gist May 30, 2015. 1 changed file with 3 additions and 2 deletions.
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    * [ImageNet Classification with Deep Convolutional Neural Networks](http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf) by Krizhevsky, Sutskever and Hinton
    * [Text Understanding from Scratch](http://arxiv.org/abs/1502.01710) by Xiang Zhang, Yann LeCun
    * [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf) by Yoshua Bengio
    * [Deep Learning - a review article that appeared in Nature](http://sci-hub.org/downloads/d397/lecun2015.pdf) by Yann LeCun, Yoshua Bengio & Geoffrey Hinton

    3. *Scalable Machine Learning*
    * [Bring the Noise: Embracing Randomness is the Key to Scaling Up Machine Learning Algorithms](http://online.liebertpub.com/doi/pdf/10.1089/big.2013.0010) by Brian Delssandro
    @@ -73,6 +74,7 @@

    8. *General*
    * [Distilling the Knowledge in a Neural Network](http://arxiv.org/abs/1503.02531) by Geoffrey Hinton, Oriol Vinyals, Jeff Dean
    * [A Random Forest Guided Tour](http://www.lsta.upmc.fr/BIAU/bs.pdf) by Biau & Scornet

    9. *MCMC*
    * [Markov Chain Monte Carlo Without all the Bullshit](http://jeremykun.com/2015/04/06/markov-chain-monte-carlo-without-all-the-bullshit/)
    @@ -83,5 +85,4 @@

    10. *Conditional Random Fields*
    * [Log-linear Models and Conditional Random Fields](http://videolectures.net/cikm08_elkan_llmacrf/) by Charles Elkan (video)
    * [Log-linear models and conditional random fields - notes](http://www.cs.columbia.edu/~smaskey/CS6998-0412/supportmaterial/cikmtutorial.pdf) by Charles Elkan
    *
    * [Log-linear models and conditional random fields - notes](http://www.cs.columbia.edu/~smaskey/CS6998-0412/supportmaterial/cikmtutorial.pdf) by Charles Elkan
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    * [Initialization of deep networks](http://deepdish.io/2015/02/24/network-initialization/)
    * [Weak Learning, Boosting, and the AdaBoost algorithm](http://jeremykun.com/2015/05/18/boosting-census/)
    * [Probably Approximately Correct — a Formal Theory of Learning](http://jeremykun.com/2014/01/02/probably-approximately-correct-a-formal-theory-of-learning/)
    * [Making sense of principal component analysis, eigenvectors & eigenvalues](http://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues)

    7. *Interesting courses and tutorials*
    * [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) at Stanford by Andrej Karpathy
    @@ -68,6 +69,7 @@
    * [Kdd 2014 Tutorial - the recommender problem revisited](http://www.slideshare.net/xamat/kdd-2014-tutorial-the-recommender-problem-revisited) by Xavier Amatriain
    * [Machine Learning 2014-15 at Oxford University](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
    * [Course Notes - Stanford Machine Learning by Andrew Ng](http://www.holehouse.org/mlclass/index.html)
    * [A Tutorial on Principal Components Analysis](http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf) by Lindsay I Smith

    8. *General*
    * [Distilling the Knowledge in a Neural Network](http://arxiv.org/abs/1503.02531) by Geoffrey Hinton, Oriol Vinyals, Jeff Dean
  3. debasishg revised this gist May 26, 2015. 1 changed file with 1 addition and 0 deletions.
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    * [A Probabilistic Theory of Deep Learning](http://arxiv.org/pdf/1504.00641v1.pdf) by Ankit B. Patel, Tan Nguyen, Richard G. Baraniuk
    * [ImageNet Classification with Deep Convolutional Neural Networks](http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf) by Krizhevsky, Sutskever and Hinton
    * [Text Understanding from Scratch](http://arxiv.org/abs/1502.01710) by Xiang Zhang, Yann LeCun
    * [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf) by Yoshua Bengio

    3. *Scalable Machine Learning*
    * [Bring the Noise: Embracing Randomness is the Key to Scaling Up Machine Learning Algorithms](http://online.liebertpub.com/doi/pdf/10.1089/big.2013.0010) by Brian Delssandro
  4. debasishg revised this gist May 22, 2015. 1 changed file with 3 additions and 0 deletions.
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    * [Ten Lessons Learned from Building (real-life impactful) Machine Learning Systems](http://technocalifornia.blogspot.in/2014/12/ten-lessons-learned-from-building-real.html)
    * [Scalable Machine Learning](http://de.slideshare.net/mikiobraun/scalable-machine-learning-47862907) by Mikio L Braun
    * [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) by Andrej Karpathy
    * [Initialization of deep networks](http://deepdish.io/2015/02/24/network-initialization/)
    * [Weak Learning, Boosting, and the AdaBoost algorithm](http://jeremykun.com/2015/05/18/boosting-census/)
    * [Probably Approximately Correct — a Formal Theory of Learning](http://jeremykun.com/2014/01/02/probably-approximately-correct-a-formal-theory-of-learning/)

    7. *Interesting courses and tutorials*
    * [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) at Stanford by Andrej Karpathy
  5. debasishg revised this gist May 21, 2015. 1 changed file with 1 addition and 0 deletions.
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    * [Extracting Structured Data From Recipes Using Conditional Random Fields](http://open.blogs.nytimes.com/2015/04/09/extracting-structured-data-from-recipes-using-conditional-random-fields/?_r=0)
    * [Ten Lessons Learned from Building (real-life impactful) Machine Learning Systems](http://technocalifornia.blogspot.in/2014/12/ten-lessons-learned-from-building-real.html)
    * [Scalable Machine Learning](http://de.slideshare.net/mikiobraun/scalable-machine-learning-47862907) by Mikio L Braun
    * [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) by Andrej Karpathy

    7. *Interesting courses and tutorials*
    * [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) at Stanford by Andrej Karpathy
  6. debasishg revised this gist May 9, 2015. 1 changed file with 2 additions and 0 deletions.
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    * [Markov Chain Monte Carlo Without all the Bullshit](http://jeremykun.com/2015/04/06/markov-chain-monte-carlo-without-all-the-bullshit/)
    * [How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?](http://stats.stackexchange.com/questions/165/how-would-you-explain-markov-chain-monte-carlo-mcmc-to-a-layperson)
    * [iTunes The Data Skeptic Podcast](https://itunes.apple.com/us/podcast/mini-markov-chain-monte-carlo/id890348705?i=339051856&mt=2)
    * [An Introduction to MCMC for Machine Learning](http://www.cs.ubc.ca/~arnaud/andrieu_defreitas_doucet_jordan_intromontecarlomachinelearning.pdf) by Christophe Andrieu, Nando De Freitas, Arnaud Daucet and Michael Jordan
    * [The Markov Chain Monte Carlo Revolution](http://statweb.stanford.edu/~cgates/PERSI/papers/MCMCRev.pdf) by Persi Diaconis

    10. *Conditional Random Fields*
    * [Log-linear Models and Conditional Random Fields](http://videolectures.net/cikm08_elkan_llmacrf/) by Charles Elkan (video)
  7. debasishg revised this gist May 9, 2015. 1 changed file with 1 addition and 0 deletions.
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    * [ECCV-2010 Tutorial: Feature Learning for Image Classification](http://ufldl.stanford.edu/eccv10-tutorial/) by Kai Yu & Andrew Ng
    * [Kdd 2014 Tutorial - the recommender problem revisited](http://www.slideshare.net/xamat/kdd-2014-tutorial-the-recommender-problem-revisited) by Xavier Amatriain
    * [Machine Learning 2014-15 at Oxford University](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
    * [Course Notes - Stanford Machine Learning by Andrew Ng](http://www.holehouse.org/mlclass/index.html)

    8. *General*
    * [Distilling the Knowledge in a Neural Network](http://arxiv.org/abs/1503.02531) by Geoffrey Hinton, Oriol Vinyals, Jeff Dean
  8. debasishg revised this gist May 8, 2015. 1 changed file with 2 additions and 1 deletion.
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    * [Rectified Linear Units Improve Restricted Boltzmann Machines](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.165.6419&rep=rep1&type=pdf) by Nair & Hinton
    * [Mathematical Intuition for Performance of Rectified Linear Unit in Deep Neural Networks](https://www.academia.edu/7826776/Mathematical_Intuition_for_Performance_of_Rectified_Linear_Unit_in_Deep_Neural_Networks) by Alexandre Dalyec

    6. *Interesting blog posts*
    6. *Interesting blog posts and presentations*
    * [Hacker's Guide to Neural Networks](https://karpathy.github.io/neuralnets/) by Andrej Karpathy
    * [Breaking Linear Classifiers on ImageNet](http://karpathy.github.io/2015/03/30/breaking-convnets/) by Andrej Karpathy
    * [Classifying plankton with Deep Neural Networks](http://benanne.github.io/2015/03/17/plankton.html)
    @@ -50,6 +50,7 @@
    * [Deep Learning vs Probabilistic Graphical Models vs Logic](http://quantombone.blogspot.in/2015/04/deep-learning-vs-probabilistic.html)
    * [Extracting Structured Data From Recipes Using Conditional Random Fields](http://open.blogs.nytimes.com/2015/04/09/extracting-structured-data-from-recipes-using-conditional-random-fields/?_r=0)
    * [Ten Lessons Learned from Building (real-life impactful) Machine Learning Systems](http://technocalifornia.blogspot.in/2014/12/ten-lessons-learned-from-building-real.html)
    * [Scalable Machine Learning](http://de.slideshare.net/mikiobraun/scalable-machine-learning-47862907) by Mikio L Braun

    7. *Interesting courses and tutorials*
    * [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) at Stanford by Andrej Karpathy
  9. debasishg revised this gist May 8, 2015. 1 changed file with 1 addition and 0 deletions.
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    * [Hash Kernels for Structured Data](http://www.jmlr.org/papers/volume10/shi09a/shi09a.pdf) by Qinfeng Shi et. al.
    * [Feature Hashing for Large Scale Multitask Learning](http://arxiv.org/pdf/0902.2206.pdf) by Weinberger et. al.
    * [Large-Scale Learning with Less RAM via Randomization](http://www.eecs.tufts.edu/~dsculley/papers/round-model-icml.pdf) by a group of authors from Google
    * [Collaborative Email-Spam Filtering with the Hashing-Trick](http://ceas.cc/2009/papers/ceas2009-paper-11.pdf) by Joshua Attenberg et. al.

    4. *Gradient based Training*
    * [Practical Recommendations for Gradient-Based Training of Deep Architectures](http://arxiv.org/pdf/1206.5533v2.pdf) by Yoshua Bengio
  10. debasishg revised this gist May 5, 2015. 1 changed file with 1 addition and 0 deletions.
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    * [Video course on Deep Learning](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) by Hugo Larochelle
    * [ECCV-2010 Tutorial: Feature Learning for Image Classification](http://ufldl.stanford.edu/eccv10-tutorial/) by Kai Yu & Andrew Ng
    * [Kdd 2014 Tutorial - the recommender problem revisited](http://www.slideshare.net/xamat/kdd-2014-tutorial-the-recommender-problem-revisited) by Xavier Amatriain
    * [Machine Learning 2014-15 at Oxford University](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)

    8. *General*
    * [Distilling the Knowledge in a Neural Network](http://arxiv.org/abs/1503.02531) by Geoffrey Hinton, Oriol Vinyals, Jeff Dean
  11. debasishg revised this gist Apr 26, 2015. 1 changed file with 4 additions and 1 deletion.
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    * [Deep Belief Networks vs Convolutional Neural Networks](http://stackoverflow.com/questions/24545725/deep-belief-networks-vs-convolutional-neural-networks)
    * [Deep Learning vs Probabilistic Graphical Models vs Logic](http://quantombone.blogspot.in/2015/04/deep-learning-vs-probabilistic.html)
    * [Extracting Structured Data From Recipes Using Conditional Random Fields](http://open.blogs.nytimes.com/2015/04/09/extracting-structured-data-from-recipes-using-conditional-random-fields/?_r=0)
    * [Ten Lessons Learned from Building (real-life impactful) Machine Learning Systems](http://technocalifornia.blogspot.in/2014/12/ten-lessons-learned-from-building-real.html)

    7. *Interesting courses*
    7. *Interesting courses and tutorials*
    * [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) at Stanford by Andrej Karpathy
    * [CS224d: Deep Learning for Natural Language Processing](http://cs224d.stanford.edu/) at Stanford by Richard Socher
    * [STA 4273H (Winter 2015): Large Scale Machine Learning](http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/) at Toronto by Russ Salakhutdinov
    * [AM 207 Monte Carlo Methods, Stochastic Optimization](http://am207.org/) at Harvard by Verena Kaynig-Fittkau and Pavlos Protopapas
    * [ACL 2012 + NAACL 2013 Tutorial: Deep Learning for NLP (without Magic)](http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial) at NAACL 2013 by Richard Socher, Chris Manning and Yoshua Bengio
    * [Video course on Deep Learning](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) by Hugo Larochelle
    * [ECCV-2010 Tutorial: Feature Learning for Image Classification](http://ufldl.stanford.edu/eccv10-tutorial/) by Kai Yu & Andrew Ng
    * [Kdd 2014 Tutorial - the recommender problem revisited](http://www.slideshare.net/xamat/kdd-2014-tutorial-the-recommender-problem-revisited) by Xavier Amatriain

    8. *General*
    * [Distilling the Knowledge in a Neural Network](http://arxiv.org/abs/1503.02531) by Geoffrey Hinton, Oriol Vinyals, Jeff Dean
  12. debasishg revised this gist Apr 23, 2015. 1 changed file with 7 additions and 1 deletion.
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    * [Recurrent Neural Networks for Collaborative Filtering](http://erikbern.com/?p=589)
    * [Deep Belief Networks vs Convolutional Neural Networks](http://stackoverflow.com/questions/24545725/deep-belief-networks-vs-convolutional-neural-networks)
    * [Deep Learning vs Probabilistic Graphical Models vs Logic](http://quantombone.blogspot.in/2015/04/deep-learning-vs-probabilistic.html)
    * [Extracting Structured Data From Recipes Using Conditional Random Fields](http://open.blogs.nytimes.com/2015/04/09/extracting-structured-data-from-recipes-using-conditional-random-fields/?_r=0)

    7. *Interesting courses*
    * [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) at Stanford by Andrej Karpathy
    @@ -62,4 +63,9 @@
    9. *MCMC*
    * [Markov Chain Monte Carlo Without all the Bullshit](http://jeremykun.com/2015/04/06/markov-chain-monte-carlo-without-all-the-bullshit/)
    * [How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?](http://stats.stackexchange.com/questions/165/how-would-you-explain-markov-chain-monte-carlo-mcmc-to-a-layperson)
    * [iTunes The Data Skeptic Podcast](https://itunes.apple.com/us/podcast/mini-markov-chain-monte-carlo/id890348705?i=339051856&mt=2)
    * [iTunes The Data Skeptic Podcast](https://itunes.apple.com/us/podcast/mini-markov-chain-monte-carlo/id890348705?i=339051856&mt=2)

    10. *Conditional Random Fields*
    * [Log-linear Models and Conditional Random Fields](http://videolectures.net/cikm08_elkan_llmacrf/) by Charles Elkan (video)
    * [Log-linear models and conditional random fields - notes](http://www.cs.columbia.edu/~smaskey/CS6998-0412/supportmaterial/cikmtutorial.pdf) by Charles Elkan
    *
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    * [Video course on Deep Learning](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) by Hugo Larochelle

    8. *General*
    * [Distilling the Knowledge in a Neural Network](http://arxiv.org/abs/1503.02531) by Geoffrey Hinton, Oriol Vinyals, Jeff Dean
    * [Distilling the Knowledge in a Neural Network](http://arxiv.org/abs/1503.02531) by Geoffrey Hinton, Oriol Vinyals, Jeff Dean

    9. *MCMC*
    * [Markov Chain Monte Carlo Without all the Bullshit](http://jeremykun.com/2015/04/06/markov-chain-monte-carlo-without-all-the-bullshit/)
    * [How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?](http://stats.stackexchange.com/questions/165/how-would-you-explain-markov-chain-monte-carlo-mcmc-to-a-layperson)
    * [iTunes The Data Skeptic Podcast](https://itunes.apple.com/us/podcast/mini-markov-chain-monte-carlo/id890348705?i=339051856&mt=2)
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    * [The devil is in the details: an evaluation of recent feature encoding methods](http://www.robots.ox.ac.uk/~vgg/publications/2011/Chatfield11/chatfield11.pdf) by Chatfield et. al.
    * [Emergence of Object-Selective Features in Unsupervised Feature Learning](http://www.cs.stanford.edu/~acoates/papers/coateskarpathyng_nips2012.pdf) by Coates, Ng
    * [Scaling Learning Algorithms towards AI](http://yann.lecun.com/exdb/publis/pdf/bengio-lecun-07.pdf) Benjio & LeCun
    * [A Theory of Feature Learning](http://arxiv.org/abs/1504.00083) by Brendan van Rooyen, Robert C. Williamson

    2. *Deep Learning*
    * [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
    @@ -17,6 +18,8 @@
    * [Towards Biologically Plausible Deep Learning](http://arxiv.org/abs/1502.04156) by Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Zhouhan Lin
    * [Training Recurrent Neural Networks](http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf) Phd thesis of Ilya Sutskever
    * [A Probabilistic Theory of Deep Learning](http://arxiv.org/pdf/1504.00641v1.pdf) by Ankit B. Patel, Tan Nguyen, Richard G. Baraniuk
    * [ImageNet Classification with Deep Convolutional Neural Networks](http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf) by Krizhevsky, Sutskever and Hinton
    * [Text Understanding from Scratch](http://arxiv.org/abs/1502.01710) by Xiang Zhang, Yann LeCun

    3. *Scalable Machine Learning*
    * [Bring the Noise: Embracing Randomness is the Key to Scaling Up Machine Learning Algorithms](http://online.liebertpub.com/doi/pdf/10.1089/big.2013.0010) by Brian Delssandro
    @@ -42,6 +45,8 @@
    * [Understanding Convolution in Deep Learning](https://timdettmers.wordpress.com/2015/03/26/convolution-deep-learning/)
    * [A Brief Overview of Deep Learning](http://yyue.blogspot.in/2015/01/a-brief-overview-of-deep-learning.html) by Ilya Sutskever
    * [Recurrent Neural Networks for Collaborative Filtering](http://erikbern.com/?p=589)
    * [Deep Belief Networks vs Convolutional Neural Networks](http://stackoverflow.com/questions/24545725/deep-belief-networks-vs-convolutional-neural-networks)
    * [Deep Learning vs Probabilistic Graphical Models vs Logic](http://quantombone.blogspot.in/2015/04/deep-learning-vs-probabilistic.html)

    7. *Interesting courses*
    * [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) at Stanford by Andrej Karpathy
    @@ -50,3 +55,6 @@
    * [AM 207 Monte Carlo Methods, Stochastic Optimization](http://am207.org/) at Harvard by Verena Kaynig-Fittkau and Pavlos Protopapas
    * [ACL 2012 + NAACL 2013 Tutorial: Deep Learning for NLP (without Magic)](http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial) at NAACL 2013 by Richard Socher, Chris Manning and Yoshua Bengio
    * [Video course on Deep Learning](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) by Hugo Larochelle

    8. *General*
    * [Distilling the Knowledge in a Neural Network](http://arxiv.org/abs/1503.02531) by Geoffrey Hinton, Oriol Vinyals, Jeff Dean
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    1. *Feature Learning*
    * [Learning Feature Representations with K-means](http://www.cs.stanford.edu/~acoates/papers/coatesng_nntot2012.pdf) by Adam Coates and Andrew Y. Ng
    * [The devil is in the details: an evaluation of recent feature encoding methods](http://www.robots.ox.ac.uk/~vgg/publications/2011/Chatfield11/chatfield11.pdf) by Chatfield et. al.
    * [Emergence of Object-Selective Features in Unsupervised Feature Learning](http://web.stanford.edu/~acoates/papers/coateskarpathyng_nips2012.pdf) by Coates, Ng
    * [Emergence of Object-Selective Features in Unsupervised Feature Learning](http://www.cs.stanford.edu/~acoates/papers/coateskarpathyng_nips2012.pdf) by Coates, Ng
    * [Scaling Learning Algorithms towards AI](http://yann.lecun.com/exdb/publis/pdf/bengio-lecun-07.pdf) Benjio & LeCun

    2. *Deep Learning*
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    * [Recurrent Neural Networks for Collaborative Filtering](http://erikbern.com/?p=589)

    7. *Interesting courses*
    * [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.github.io/) at Stanford by Andrej Karpathy
    * [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) at Stanford by Andrej Karpathy
    * [CS224d: Deep Learning for Natural Language Processing](http://cs224d.stanford.edu/) at Stanford by Richard Socher
    * [STA 4273H (Winter 2015): Large Scale Machine Learning](http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/) at Toronto by Russ Salakhutdinov
    * [AM 207 Monte Carlo Methods, Stochastic Optimization](http://am207.org/) at Harvard by Verena Kaynig-Fittkau and Pavlos Protopapas
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    * [Emergence of Object-Selective Features in Unsupervised Feature Learning](http://web.stanford.edu/~acoates/papers/coateskarpathyng_nips2012.pdf) by Coates, Ng
    * [Scaling Learning Algorithms towards AI](http://yann.lecun.com/exdb/publis/pdf/bengio-lecun-07.pdf) Benjio & LeCun

    2. *Deep Neural Nets*
    2. *Deep Learning*
    * [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
    * [Understanding the difficulty of training deep feedforward neural networks](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf) by Xavier Glorot and Yoshua Bengio
    * [On the difficulty of training Recurrent Neural Networks](http://arxiv.org/pdf/1211.5063v2.pdf) by Razvan Pascanu, Tomas Mikolov and Yoshua Bengio
    @@ -16,6 +16,7 @@
    * [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) by LeCun, Bottou et al
    * [Towards Biologically Plausible Deep Learning](http://arxiv.org/abs/1502.04156) by Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Zhouhan Lin
    * [Training Recurrent Neural Networks](http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf) Phd thesis of Ilya Sutskever
    * [A Probabilistic Theory of Deep Learning](http://arxiv.org/pdf/1504.00641v1.pdf) by Ankit B. Patel, Tan Nguyen, Richard G. Baraniuk

    3. *Scalable Machine Learning*
    * [Bring the Noise: Embracing Randomness is the Key to Scaling Up Machine Learning Algorithms](http://online.liebertpub.com/doi/pdf/10.1089/big.2013.0010) by Brian Delssandro
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    * [STA 4273H (Winter 2015): Large Scale Machine Learning](http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/) at Toronto by Russ Salakhutdinov
    * [AM 207 Monte Carlo Methods, Stochastic Optimization](http://am207.org/) at Harvard by Verena Kaynig-Fittkau and Pavlos Protopapas
    * [ACL 2012 + NAACL 2013 Tutorial: Deep Learning for NLP (without Magic)](http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial) at NAACL 2013 by Richard Socher, Chris Manning and Yoshua Bengio
    * [Video course on Deep Learning](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) by Hugo Larochelle
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    * [CS224d: Deep Learning for Natural Language Processing](http://cs224d.stanford.edu/) at Stanford by Richard Socher
    * [STA 4273H (Winter 2015): Large Scale Machine Learning](http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/) at Toronto by Russ Salakhutdinov
    * [AM 207 Monte Carlo Methods, Stochastic Optimization](http://am207.org/) at Harvard by Verena Kaynig-Fittkau and Pavlos Protopapas
    * [ACL 2012 + NAACL 2013 Tutorial: Deep Learning for NLP (without Magic)](http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial) at NAACL 2013 by Richard Socher, Chris Manning and Yoshua Bengio
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    6. *Interesting blog posts*
    * [Hacker's Guide to Neural Networks](https://karpathy.github.io/neuralnets/) by Andrej Karpathy
    * [Breaking Linear Classifiers on ImageNet](http://karpathy.github.io/2015/03/30/breaking-convnets/) by Andrej Karpathy
    * [Classifying plankton with Deep Neural Networks](http://benanne.github.io/2015/03/17/plankton.html)
    * [Deep stuff about deep learning?](https://blogs.princeton.edu/imabandit/2015/03/20/deep-stuff-about-deep-learning/)
    * [Understanding Convolution in Deep Learning](https://timdettmers.wordpress.com/2015/03/26/convolution-deep-learning/)
    * [A Brief Overview of Deep Learning](http://yyue.blogspot.in/2015/01/a-brief-overview-of-deep-learning.html) by Ilya Sutskever
    * [Recurrent Neural Networks for Collaborative Filtering](http://erikbern.com/?p=589)


    7. *Interesting courses*
    * [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.github.io/) at Stanford by Andrej Karpathy
    * [CS224d: Deep Learning for Natural Language Processing](http://cs224d.stanford.edu/) at Stanford by Richard Socher
    * [STA 4273H (Winter 2015): Large Scale Machine Learning](http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/) at Toronto by Russ Salakhutdinov
    * [AM 207 Monte Carlo Methods, Stochastic Optimization](http://am207.org/) at Harvard by Verena Kaynig-Fittkau and Pavlos Protopapas
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    5. *Non Linear Units*
    * [Rectified Linear Units Improve Restricted Boltzmann Machines](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.165.6419&rep=rep1&type=pdf) by Nair & Hinton
    * [Mathematical Intuition for Performance of Rectified Linear Unit in Deep Neural Networks](https://www.academia.edu/7826776/Mathematical_Intuition_for_Performance_of_Rectified_Linear_Unit_in_Deep_Neural_Networks) by Alexandre Dalyec
    *

    6. *Interesting blog posts*
    * [Hacker's Guide to Neural Networks](https://karpathy.github.io/neuralnets/) by Andrej Karpathy
    * [Classifying plankton with Deep Neural Networks](http://benanne.github.io/2015/03/17/plankton.html)
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    5. *Non Linear Units*
    * [Rectified Linear Units Improve Restricted Boltzmann Machines](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.165.6419&rep=rep1&type=pdf) by Nair & Hinton
    * [Mathematical Intuition for Performance of Rectified Linear Unit in Deep Neural Networks](https://www.academia.edu/7826776/Mathematical_Intuition_for_Performance_of_Rectified_Linear_Unit_in_Deep_Neural_Networks) by Alexandre Dalyec
    *
    6. *Interesting blog posts*
    * [Hacker's Guide to Neural Networks](https://karpathy.github.io/neuralnets/) by Andrej Karpathy
    * [Classifying plankton with Deep Neural Networks](http://benanne.github.io/2015/03/17/plankton.html)
    * [Deep stuff about deep learning?](https://blogs.princeton.edu/imabandit/2015/03/20/deep-stuff-about-deep-learning/)
    * [Understanding Convolution in Deep Learning](https://timdettmers.wordpress.com/2015/03/26/convolution-deep-learning/)
    * [A Brief Overview of Deep Learning](http://yyue.blogspot.in/2015/01/a-brief-overview-of-deep-learning.html) by Ilya Sutskever
    * [Recurrent Neural Networks for Collaborative Filtering](http://erikbern.com/?p=589)

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    * [The TradeOffs of Large Scale Learning](http://papers.nips.cc/paper/3323-the-tradeoffs-of-large-scale-learning.pdf) by Leon Bottou & Olivier Bousquet
    * [Hash Kernels for Structured Data](http://www.jmlr.org/papers/volume10/shi09a/shi09a.pdf) by Qinfeng Shi et. al.
    * [Feature Hashing for Large Scale Multitask Learning](http://arxiv.org/pdf/0902.2206.pdf) by Weinberger et. al.
    * [Large-Scale Learning with Less RAM via Randomization](http://www.eecs.tufts.edu/~dsculley/papers/round-model-icml.pdf) by a group of authors from Google

    4. *Gradient based Training*
    * [Practical Recommendations for Gradient-Based Training of Deep Architectures](http://arxiv.org/pdf/1206.5533v2.pdf) by Yoshua Bengio
  24. debasishg revised this gist Mar 23, 2015. 1 changed file with 10 additions and 2 deletions.
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    * [On the difficulty of training Recurrent Neural Networks](http://arxiv.org/pdf/1211.5063v2.pdf) by Razvan Pascanu, Tomas Mikolov and Yoshua Bengio
    * [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](http://arxiv.org/abs/1502.03167) by Sergey Ioffe and Christian Szegedy
    * [Deep Learning in Neural Networks: An Overview](http://arxiv.org/pdf/1404.7828v4.pdf) by Jurgen Schmidhuber
    * [Stochastic Gradient Descent Tricks](http://research.microsoft.com/pubs/192769/tricks-2012.pdf) by L´eon Bottou
    * [Qualitatively characterizing neural network optimization problems](http://arxiv.org/abs/1412.6544) by Ian J. Goodfellow, Oriol Vinyals
    * [On Recurrent and Deep Neural Networks](http://www-etud.iro.umontreal.ca/~pascanur/papers/thesis.pdf) Phd thesis of Razvan Pascanu
    * [Scaling Learning Algorithms towards AI](http://yann.lecun.com/exdb/publis/pdf/bengio-lecun-07.pdf) by Yann LeCun and Yoshua Benjio
    * [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) by LeCun, Bottou et al
    * [Towards Biologically Plausible Deep Learning](http://arxiv.org/abs/1502.04156) by Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Zhouhan Lin
    * [Training Recurrent Neural Networks](http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf) Phd thesis of Ilya Sutskever

    2. *Scalable Machine Learning*
    3. *Scalable Machine Learning*
    * [Bring the Noise: Embracing Randomness is the Key to Scaling Up Machine Learning Algorithms](http://online.liebertpub.com/doi/pdf/10.1089/big.2013.0010) by Brian Delssandro
    * [Large Scale Machine Learning with Stochastic Gradient Descent](http://leon.bottou.org/publications/pdf/compstat-2010.pdf) by Leon Bottou
    * [The TradeOffs of Large Scale Learning](http://papers.nips.cc/paper/3323-the-tradeoffs-of-large-scale-learning.pdf) by Leon Bottou & Olivier Bousquet
    * [Hash Kernels for Structured Data](http://www.jmlr.org/papers/volume10/shi09a/shi09a.pdf) by Qinfeng Shi et. al.
    * [Feature Hashing for Large Scale Multitask Learning](http://arxiv.org/pdf/0902.2206.pdf) by Weinberger et. al.

    4. *Gradient based Training*
    * [Practical Recommendations for Gradient-Based Training of Deep Architectures](http://arxiv.org/pdf/1206.5533v2.pdf) by Yoshua Bengio
    * [Stochastic Gradient Descent Tricks](http://research.microsoft.com/pubs/192769/tricks-2012.pdf) by L´eon Bottou

    5. *Non Linear Units*
    * [Rectified Linear Units Improve Restricted Boltzmann Machines](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.165.6419&rep=rep1&type=pdf) by Nair & Hinton
    * [Mathematical Intuition for Performance of Rectified Linear Unit in Deep Neural Networks](https://www.academia.edu/7826776/Mathematical_Intuition_for_Performance_of_Rectified_Linear_Unit_in_Deep_Neural_Networks) by Alexandre Dalyec

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    * [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) by LeCun, Bottou et al
    * [Towards Biologically Plausible Deep Learning](http://arxiv.org/abs/1502.04156) by Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Zhouhan Lin
    * [Training Recurrent Neural Networks](http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf) Phd thesis of Ilya Sutskever

    2. *Scalable Machine Learning*
    * [Bring the Noise: Embracing Randomness is the Key to Scaling Up Machine Learning Algorithms](http://online.liebertpub.com/doi/pdf/10.1089/big.2013.0010) by Brian Delssandro
    * [Large Scale Machine Learning with Stochastic Gradient Descent](http://leon.bottou.org/publications/pdf/compstat-2010.pdf) by Leon Bottou
    * [The TradeOffs of Large Scale Learning](http://papers.nips.cc/paper/3323-the-tradeoffs-of-large-scale-learning.pdf) by Leon Bottou & Olivier Bousquet
    * [Hash Kernels for Structured Data](http://www.jmlr.org/papers/volume10/shi09a/shi09a.pdf) by Qinfeng Shi et. al.
    * [Feature Hashing for Large Scale Multitask Learning](http://arxiv.org/pdf/0902.2206.pdf) by Weinberger et. al.
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    * [Scaling Learning Algorithms towards AI](http://yann.lecun.com/exdb/publis/pdf/bengio-lecun-07.pdf) by Yann LeCun and Yoshua Benjio
    * [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) by LeCun, Bottou et al
    * [Towards Biologically Plausible Deep Learning](http://arxiv.org/abs/1502.04156) by Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Zhouhan Lin
    * [Training Recurrent Neural Networks](http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf) Phd thesis of Ilya Sutskever
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    * [Stochastic Gradient Descent Tricks](http://research.microsoft.com/pubs/192769/tricks-2012.pdf) by L´eon Bottou
    * [Qualitatively characterizing neural network optimization problems](http://arxiv.org/abs/1412.6544) by Ian J. Goodfellow, Oriol Vinyals
    * [On Recurrent and Deep Neural Networks](http://www-etud.iro.umontreal.ca/~pascanur/papers/thesis.pdf) Phd thesis of Razvan Pascanu
    * [Scaling Learning Algorithms towards AI](http://yann.lecun.com/exdb/publis/pdf/bengio-lecun-07.pdf) by Yann LeCun and Yoshua Benjio
    * [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) by LeCun, Bottou et al
    * [Towards Biologically Plausible Deep Learning](http://arxiv.org/abs/1502.04156) by Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Zhouhan Lin
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    * [Deep Learning in Neural Networks: An Overview](http://arxiv.org/pdf/1404.7828v4.pdf) by Jurgen Schmidhuber
    * [Stochastic Gradient Descent Tricks](http://research.microsoft.com/pubs/192769/tricks-2012.pdf) by L´eon Bottou
    * [Qualitatively characterizing neural network optimization problems](http://arxiv.org/abs/1412.6544) by Ian J. Goodfellow, Oriol Vinyals
    * [On Recurrent and Deep Neural Networks](http://www-etud.iro.umontreal.ca/~pascanur/papers/thesis.pdf) Phd thesis of Razvan Pascanu
  29. debasishg revised this gist Feb 21, 2015. 1 changed file with 1 addition and 1 deletion.
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    * [Learning Feature Representations with K-means](http://www.cs.stanford.edu/~acoates/papers/coatesng_nntot2012.pdf) by Adam Coates and Andrew Y. Ng
    * [The devil is in the details: an evaluation of recent feature encoding methods](http://www.robots.ox.ac.uk/~vgg/publications/2011/Chatfield11/chatfield11.pdf) by Chatfield et. al.
    * [Emergence of Object-Selective Features in Unsupervised Feature Learning](http://web.stanford.edu/~acoates/papers/coateskarpathyng_nips2012.pdf) by Coates, Ng
    * [Scaling Learning Algorithms towards AI](http://yann.lecun.com/exdb/publis/pdf/bengio-lecun-07.pdf) Bnejio & LeCun
    * [Scaling Learning Algorithms towards AI](http://yann.lecun.com/exdb/publis/pdf/bengio-lecun-07.pdf) Benjio & LeCun

    2. *Deep Neural Nets*
    * [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
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    * [Learning Feature Representations with K-means](http://www.cs.stanford.edu/~acoates/papers/coatesng_nntot2012.pdf) by Adam Coates and Andrew Y. Ng
    * [The devil is in the details: an evaluation of recent feature encoding methods](http://www.robots.ox.ac.uk/~vgg/publications/2011/Chatfield11/chatfield11.pdf) by Chatfield et. al.
    * [Emergence of Object-Selective Features in Unsupervised Feature Learning](http://web.stanford.edu/~acoates/papers/coateskarpathyng_nips2012.pdf) by Coates, Ng
    * [Scaling Learning Algorithms towards AI](http://yann.lecun.com/exdb/publis/pdf/bengio-lecun-07.pdf) Bnejio & LeCun

    2. *Deep Neural Nets*
    * [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov