└── README.md /README.md: -------------------------------------------------------------------------------- 1 | Deep Learning 2 | ============= 3 | 4 | Deep Learning Reading List of some of the materials i found on the web for Deep Learning beginners. 5 | 6 | Free Online Books 7 | ----------------- 8 | 9 | - [Deep Learning](http://www.iro.umontreal.ca/~bengioy/dlbook/) *by Yoshua Bengio, Ian Goodfellow and Aaron Courville* 10 | - [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) *by Michael Nielsen* 11 | - [Deep Learning](http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf) *by Microsoft Research* 12 | - [Deep Learning Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf) *by LISA lab, University of Montreal* 13 | 14 | 15 | Courses 16 | ------- 17 | 18 | - [Machine Learning](https://class.coursera.org/ml-005) *by Andrew Ng in Coursera* 19 | - [Neural Networks](https://class.coursera.org/neuralnets-2012-001) *for Machine Learning by Geoffrey Hinton in Coursera* 20 | - [Neural networks class](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) *by Hugo Larochelle from Université de Sherbrooke* 21 | - [Deep Learning Course](http://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start) *by CILVR lab @ NYU* 22 | 23 | 24 | Video and Lectures 25 | ------------------ 26 | 27 | - [How To Create A Mind](https://www.youtube.com/watch?v=RIkxVci-R4k) *By Ray Kurzweil - Is a inspiring talk* 28 | - [Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24) *By Andrew Ng* 29 | - [Recent Developments in Deep Learning](https://www.youtube.com/watch?v=vShMxxqtDDs&index=3&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT) *By Geoff Hinton* 30 | - [The Unreasonable Effectiveness of Deep Learning](https://www.youtube.com/watch?v=sc-KbuZqGkI) *by Yann LeCun* 31 | - [Deep Learning of Representations](https://www.youtube.com/watch?v=4xsVFLnHC_0) *by Yoshua bengio* 32 | - [Principles of Hierarchical Temporal Memory](https://www.youtube.com/watch?v=6ufPpZDmPKA) *by Jeff Hawkins* 33 | - [Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab](https://www.youtube.com/watch?v=2QJi0ArLq7s&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT) *by Adam Coates* 34 | - [Making Sense of the World with Deep Learning](http://vimeo.com/80821560) *By Adam Coates* 35 | - [Demystifying Unsupervised Feature Learning](https://www.youtube.com/watch?v=wZfVBwOO0-k) *By Adam Coates* 36 | - [Visual Perception with Deep Learning](https://www.youtube.com/watch?v=3boKlkPBckA) *By Yann LeCun* 37 | 38 | 39 | Papers 40 | ------ 41 | 42 | - [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) 43 | - [Using Very Deep Autoencoders for Content Based Image Retrieval](http://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf) 44 | - [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf) 45 | - [CMU’s list of papers](http://deeplearning.cs.cmu.edu/) 46 | 47 | 48 | Tutorials 49 | --------- 50 | 51 | - [UFLDL Tutorial 1](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial) 52 | - [UFLDL Tutorial 2](http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/) 53 | - [Deep Learning for NLP](http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial) 54 | - [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) 55 | 56 | 57 | WebSites 58 | -------- 59 | 60 | - [deeplearning.net](http://deeplearning.net/) 61 | - [deeplearning.stanford.edu](http://deeplearning.stanford.edu/) 62 | 63 | 64 | Datasets 65 | -------- 66 | 67 | - [MNIST Handwritten digits](http://yann.lecun.com/exdb/mnist/) 68 | - [Google House Numbers from street view](http://ufldl.stanford.edu/housenumbers/) 69 | - [CIFAR-10 and CIFAR-100](http://www.cs.toronto.edu/~kriz/cifar.html) 70 | - [IMAGENET](http://www.image-net.org/) 71 | - [Tiny Images 80 Million tiny images](http://groups.csail.mit.edu/vision/TinyImages/) 72 | - [Flickr Data 100 Million Yahoo dataset](http://yahoolabs.tumblr.com/post/89783581601/one-hundred-million-creative-commons-flickr-images) 73 | - [Berkeley Segmentation Dataset 500](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/) 74 | 75 | 76 | Frameworks 77 | ---------- 78 | 79 | - [Caffe](http://caffe.berkeleyvision.org/) 80 | - [Torch7](http://torch.ch/) 81 | - [Theano](http://deeplearning.net/software/theano/) 82 | - [cuda-convnet](https://code.google.com/p/cuda-convnet2/) 83 | - [Ccv](http://libccv.org/doc/doc-convnet/) 84 | - [NuPIC](http://numenta.org/nupic.html) 85 | - [DeepLearning4J](http://deeplearning4j.org/) 86 | 87 | 88 | Miscellaneous 89 | ------------- 90 | 91 | - [Google Plus - Deep Learning Community](https://plus.google.com/communities/112866381580457264725) 92 | - [Caffe Webinar](http://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.php?searchByKeyword=shelhamer&searchItems=&sessionTopic=&sessionEvent=4&sessionYear=2014&sessionFormat=&submit=&select=+) 93 | - [100 Best Github Resources in Github for DL](http://meta-guide.com/software-meta-guide/100-best-github-deep-learning/) 94 | - [Word2Vec](https://code.google.com/p/word2vec/) 95 | - [Caffe DockerFile](https://registry.hub.docker.com/u/tleyden5iwx/caffe/) 96 | - [TorontoDeepLEarning convnet](https://github.com/TorontoDeepLearning/convnet) 97 | - [Vision data sets](http://www.cs.cmu.edu/~cil/v-images.html) 98 | - [Fantastic Torch Tutorial](http://code.cogbits.com/wiki/doku.php) 99 | 100 | --------------------------------------------------------------------------------