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+# Deep learning Reading List
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+Following is a growing list of some of the materials i found on the web for Deep Learning beginners.
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### Free Online Books
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-1. [Deep Learning][] by Yoshua Bengio, Ian Goodfellow and Aaron
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- Courville
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-2. [Neural Networks and Deep Learning][] by Michael Nielsen
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-3. [Deep Learning][1] by Microsoft Research
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-4. [Deep Learning Tutorial][] by LISA lab, University of Montreal
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+1. [Deep Learning](http://www.iro.umontreal.ca/~bengioy/dlbook/) by Yoshua Bengio, Ian Goodfellow and Aaron Courville
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+2. [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) by Michael Nielsen
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+3. [Deep Learning](http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf) by Microsoft Research
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+4. [Deep Learning Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf) by LISA lab, University of Montreal
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### Courses
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-1. [Machine Learning][] by Andrew Ng in Coursera
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-2. [Neural Networks for Machine Learning][] by Geoffrey Hinton in
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- Coursera
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-3. [Neural networks class][] by Hugo Larochelle from Université de
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- Sherbrooke
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-4. [Deep Learning Course][] by CILVR lab @ NYU
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+1. [Machine Learning](https://class.coursera.org/ml-005) by Andrew Ng in Coursera
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+2. [Neural Networks for Machine Learning](https://class.coursera.org/neuralnets-2012-001) by Geoffrey Hinton in Coursera
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+3. [Neural networks class](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) by Hugo Larochelle from Université de Sherbrooke
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+4. [Deep Learning Course](http://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start) by CILVR lab @ NYU
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### Video and Lectures
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-1. [How To Create A Mind][] By Ray Kurzweil - Is a inspiring talk
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-2. [Deep Learning, Self-Taught Learning and Unsupervised Feature
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- Learning][] By Andrew Ng
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-3. [Recent Developments in Deep Learning][] By Geoff Hinton
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-4. [The Unreasonable Effectiveness of Deep Learning][] by Yann LeCun
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-5. [Deep Learning of Representations][] by Yoshua bengio
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-6. [Principles of Hierarchical Temporal Memory][] by Jeff Hawkins
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-7. [Machine Learning Discussion Group - Deep Learning w/ Stanford AI
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- Lab][] by Adam Coates
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-8. [Making Sense of the World with Deep Learning][] By Adam Coates
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-9. [Demystifying Unsupervised Feature Learning][] By Adam Coates
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-10. [Visual Perception with Deep Learning][] By Yann LeCun
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+1. [How To Create A Mind](https://www.youtube.com/watch?v=RIkxVci-R4k) By Ray Kurzweil - Is a inspiring talk2. [Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24) By Andrew Ng
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+3. [Recent Developments in Deep Learning](https://www.youtube.com/watch?v=vShMxxqtDDs&index=3&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT) By Geoff Hinton
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+4. [The Unreasonable Effectiveness of Deep Learning](https://www.youtube.com/watch?v=sc-KbuZqGkI) by Yann LeCun
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+5. [Deep Learning of Representations](https://www.youtube.com/watch?v=4xsVFLnHC_0) by Yoshua bengio
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+6. [Principles of Hierarchical Temporal Memory](https://www.youtube.com/watch?v=6ufPpZDmPKA) by Jeff Hawkins
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+7. [Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab](https://www.youtube.com/watch?v=2QJi0ArLq7s&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT) by Adam Coates
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+8. [Making Sense of the World with Deep Learning](http://vimeo.com/80821560) By Adam Coates
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+9. [Demystifying Unsupervised Feature Learning ](https://www.youtube.com/watch?v=wZfVBwOO0-k) By Adam Coates
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+10. [Visual Perception with Deep Learning](https://www.youtube.com/watch?v=3boKlkPBckA) By Yann LeCun
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### Papers
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-</div>
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+1. [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
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+2. [Using Very Deep Autoencoders for Content Based Image Retrieval](http://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf)
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+3. [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf)
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+4. [CMU’s list of papers](http://deeplearning.cs.cmu.edu/)
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-</div>
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+### Tutorials
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+
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+1. [UFLDL Tutorial 1](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial)
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+2. [UFLDL Tutorial 2](http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/)
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+3. [Deep Learning for NLP (without Magic)](http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial)
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+4. [A Deep Learning Tutorial: From Perceptrons to Deep Networks](http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks)
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+
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+### WebSites
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+
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+1. [deeplearning.net](http://deeplearning.net/)
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+2. [deeplearning.stanford.edu](http://deeplearning.stanford.edu/)
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+
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+### Datasets
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+
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+1. [MNIST](http://yann.lecun.com/exdb/mnist/) Handwritten digits
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+2. [Google House Numbers](http://ufldl.stanford.edu/housenumbers/) from street view
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+3. [CIFAR-10 and CIFAR-100](http://www.cs.toronto.edu/~kriz/cifar.html)4. [IMAGENET](http://www.image-net.org/)
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+5. [Tiny Images](http://groups.csail.mit.edu/vision/TinyImages/) 80 Million tiny images6. [Flickr Data](http://yahoolabs.tumblr.com/post/89783581601/one-hundred-million-creative-commons-flickr-images) 100 Million Yahoo dataset
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+7. [Berkeley Segmentation Dataset 500](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/)
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+
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+### Frameworks
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+
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+1. [Caffe](http://caffe.berkeleyvision.org/)2. [Torch7](http://torch.ch/)
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+3. [Theano](http://deeplearning.net/software/theano/)
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+4. [cuda-convnet](https://code.google.com/p/cuda-convnet2/)5. [Ccv](http://libccv.org/doc/doc-convnet/)
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+6. [NuPIC](http://numenta.org/nupic.html)
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+7. [DeepLearning4J](http://deeplearning4j.org/)
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+
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+### Miscellaneous
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+
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+1. [Google Plus - Deep Learning Community](https://plus.google.com/communities/112866381580457264725)
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+2. [Caffe Webinar](http://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.php?searchByKeyword=shelhamer&searchItems=&sessionTopic=&sessionEvent=4&sessionYear=2014&sessionFormat=&submit=&select=+)
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+3. [100 Best Github Resources in Github for DL](http://meta-guide.com/software-meta-guide/100-best-github-deep-learning/)
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+4. [Word2Vec](https://code.google.com/p/word2vec/)
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+5. [Caffe DockerFile](https://registry.hub.docker.com/u/tleyden5iwx/caffe/)
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+6. [TorontoDeepLEarning convnet](https://github.com/TorontoDeepLearning/convnet)
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+7. [Vision data sets](http://www.cs.cmu.edu/~cil/v-images.html)
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+8. [Fantastic Torch Tutorial](http://code.cogbits.com/wiki/doku.php) My personal favourite. Also check out [gfx.js](https://github.com/clementfarabet/gfx.js)
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+9. [Torch7 Cheat sheet](https://github.com/torch/torch7/wiki/Cheatsheet)
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+
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+ </article>
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</div>
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- [Deep Learning]: http://www.iro.umontreal.ca/~bengioy/dlbook/
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- [Neural Networks and Deep Learning]: http://neuralnetworksanddeeplearning.com/
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- [1]: http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf
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- [Deep Learning Tutorial]: http://deeplearning.net/tutorial/deeplearning.pdf
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- [Machine Learning]: https://class.coursera.org/ml-005
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- [Neural Networks for Machine Learning]: https://class.coursera.org/neuralnets-2012-001
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- [Neural networks class]: https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH
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- [Deep Learning Course]: http://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start
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- [How To Create A Mind]: https://www.youtube.com/watch?v=RIkxVci-R4k
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- [Deep Learning, Self-Taught Learning and Unsupervised Feature
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- Learning]: https://www.youtube.com/watch?v=n1ViNeWhC24
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- [Recent Developments in Deep Learning]: https://www.youtube.com/watch?v=vShMxxqtDDs&index=3&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT
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- [The Unreasonable Effectiveness of Deep Learning]: https://www.youtube.com/watch?v=sc-KbuZqGkI
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- [Deep Learning of Representations]: https://www.youtube.com/watch?v=4xsVFLnHC_0
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- [Principles of Hierarchical Temporal Memory]: https://www.youtube.com/watch?v=6ufPpZDmPKA
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- [Machine Learning Discussion Group - Deep Learning w/ Stanford AI
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- Lab]: https://www.youtube.com/watch?v=2QJi0ArLq7s&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT
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- [Making Sense of the World with Deep Learning]: http://vimeo.com/80821560
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- [Demystifying Unsupervised Feature Learning]: https://www.youtube.com/watch?v=wZfVBwOO0-k
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- [Visual Perception with Deep Learning]: https://www.youtube.com/watch?v=3boKlkPBckA
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+ </div>
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+ </div>
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