### Free Online Books

1.  [Deep Learning](http://www.iro.umontreal.ca/~bengioy/dlbook/) by Yoshua Bengio, Ian Goodfellow and Aaron Courville  (01/01/2015)
2.  [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) by  Michael Nielsen (Dec 2014)
3.  [Deep Learning](http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf) by Microsoft Research (2013) 
4.  [Deep Learning Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf) by LISA lab, University of Montreal (Jan 6 2015)
 
### Courses

1.  [Machine Learning - Stanford](https://class.coursera.org/ml-005) by Andrew Ng in Coursera (2010-2014)
2.  [Machine Learning - Caltech](http://work.caltech.edu/lectures.html) by Yaser Abu-Mostafa (2012-2014)
3.  [Machine Learning - Carnegie Mellon](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml) by Tom Mitchell (Spring 2011)
2.  [Neural Networks for Machine Learning](https://class.coursera.org/neuralnets-2012-001) by Geoffrey Hinton in Coursera (2012)
3.  [Neural networks class](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) by Hugo Larochelle from Université de Sherbrooke (2013)
4.  [Deep Learning Course](http://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start) by CILVR lab @ NYU (2014)
5.  [A.I - Berkeley](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/courseware/) by Dan Klein and Pieter Abbeel (2013)
6.  [A.I - MIT](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/) by Patrick Henry Winston (2010)
7.  [Vision and learning - computers and brains](http://web.mit.edu/course/other/i2course/www/vision_and_learning_fall_2013.html) by Shimon Ullman, Tomaso Poggio, Ethan Meyers @ MIT (2013)
8.  [Convolutional Neural Networks for Visual Recognition - Stanford](http://vision.stanford.edu/teaching/cs231n/syllabus.html) by Fei-Fei Li, Andrej Karpathy (2015)


### Videos and Lectures

1.  [How To Create A Mind](https://www.youtube.com/watch?v=RIkxVci-R4k) By Ray Kurzweil
2.  [Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24) By Andrew Ng
3.  [Recent Developments in Deep Learning](https://www.youtube.com/watch?v=vShMxxqtDDs&index=3&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT) By Geoff Hinton
4.  [The Unreasonable Effectiveness of Deep Learning](https://www.youtube.com/watch?v=sc-KbuZqGkI) by Yann LeCun
5.  [Deep Learning of Representations](https://www.youtube.com/watch?v=4xsVFLnHC_0) by Yoshua bengio
6.  [Principles of Hierarchical Temporal Memory](https://www.youtube.com/watch?v=6ufPpZDmPKA) by Jeff Hawkins
7.  [Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab](https://www.youtube.com/watch?v=2QJi0ArLq7s&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT) by Adam Coates
8.  [Making Sense of the World with Deep Learning](http://vimeo.com/80821560) By Adam Coates 
9.  [Demystifying Unsupervised Feature Learning ](https://www.youtube.com/watch?v=wZfVBwOO0-k) By Adam Coates
10.  [Visual Perception with Deep Learning](https://www.youtube.com/watch?v=3boKlkPBckA) By Yann LeCun
11.  [The Next Generation of Neural Networks](https://www.youtube.com/watch?v=AyzOUbkUf3M) By Geoffrey Hinton at GoogleTechTalks
12.  [The wonderful and terrifying implications of computers that can learn](http://www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn) By Jeremy Howard at TEDxBrussels

### Papers

1.  [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
2.  [Using Very Deep Autoencoders for Content Based Image Retrieval](http://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf)
3.  [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf)
4.  [CMU’s list of papers](http://deeplearning.cs.cmu.edu/)

### Tutorials

1.  [UFLDL Tutorial 1](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial)
2.  [UFLDL Tutorial 2](http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/)
3.  [Deep Learning for NLP (without Magic)](http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial)
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)
5.  [Deep Learning from the Bottom up](http://www.metacademy.org/roadmaps/rgrosse/deep_learning)
6.  [Theano Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf)



### WebSites

1.  [deeplearning.net](http://deeplearning.net/)
2.  [deeplearning.stanford.edu](http://deeplearning.stanford.edu/)

### Datasets

1.  [MNIST](http://yann.lecun.com/exdb/mnist/) Handwritten digits
2.  [Google House Numbers](http://ufldl.stanford.edu/housenumbers/) from street view
3.  [CIFAR-10 and CIFAR-100](http://www.cs.toronto.edu/~kriz/cifar.html)4.  
4.  [IMAGENET](http://www.image-net.org/)
5.  [Tiny Images](http://groups.csail.mit.edu/vision/TinyImages/) 80 Million tiny images6.  
6.  [Flickr Data](http://yahoolabs.tumblr.com/post/89783581601/one-hundred-million-creative-commons-flickr-images) 100 Million Yahoo dataset
7.  [Berkeley Segmentation Dataset 500](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/)

### Frameworks

1.  [Caffe](http://caffe.berkeleyvision.org/)  
2.  [Torch7](http://torch.ch/)
3.  [Theano](http://deeplearning.net/software/theano/)
4.  [cuda-convnet](https://code.google.com/p/cuda-convnet2/)
5.  [convetjs](https://github.com/karpathy/convnetjs)
5.  [Ccv](http://libccv.org/doc/doc-convnet/)
6.  [NuPIC](http://numenta.org/nupic.html)
7.  [DeepLearning4J](http://deeplearning4j.org/)
8.  [Brain](https://github.com/harthur/brain)
9.  [DeepLearnToolbox](https://github.com/rasmusbergpalm/DeepLearnToolbox)
10.  [Deepnet](https://github.com/nitishsrivastava/deepnet)
11.  [Deeppy](https://github.com/andersbll/deeppy)
12.  [JavaNN](https://github.com/ivan-vasilev/neuralnetworks)
13.  [hebel](https://github.com/hannes-brt/hebel)
14.  [Mocha.jl](https://github.com/pluskid/Mocha.jl)
15.  [OpenDL](https://github.com/guoding83128/OpenDL)

### Miscellaneous

1.  [Google Plus - Deep Learning Community](https://plus.google.com/communities/112866381580457264725)
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=+)
3.  [100 Best Github Resources in Github for DL](http://meta-guide.com/software-meta-guide/100-best-github-deep-learning/)
4.  [Word2Vec](https://code.google.com/p/word2vec/)
5.  [Caffe DockerFile](https://registry.hub.docker.com/u/tleyden5iwx/caffe/)
6.  [TorontoDeepLEarning convnet](https://github.com/TorontoDeepLearning/convnet)
7.  [Vision data sets](http://www.cs.cmu.edu/~cil/v-images.html)
8.  [Fantastic Torch Tutorial](http://code.cogbits.com/wiki/doku.php)
9.  [gfx.js](https://github.com/clementfarabet/gfx.js)
10. [Torch7 Cheat sheet](https://github.com/torch/torch7/wiki/Cheatsheet)
11. [Misc from MIT's 'Advanced Natural Language Processing' course] (http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-864-advanced-natural-language-processing-fall-2005/)
12. [Misc from MIT's 'Machine Learning' course](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/)
13. [Misc from MIT's 'Networks for Learning: Regression and Classification' course](http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-520-a-networks-for-learning-regression-and-classification-spring-2001/)
14. [Misc from MIT's 'Neural Coding and Perception of Sound' course](http://ocw.mit.edu/courses/health-sciences-and-technology/hst-723j-neural-coding-and-perception-of-sound-spring-2005/index.htm)
15. [Implementing a Distributed Deep Learning Network over Spark](http://www.datasciencecentral.com/profiles/blogs/implementing-a-distributed-deep-learning-network-over-spark)
16. [A chess AI that learns to play chess using deep learning.](https://github.com/erikbern/deep-pink)
17. Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind (https://github.com/kristjankorjus/Replicating-DeepMind)


-----
### Contributing
Have anything in mind that you think is awesome and would fit in this list? Feel free to send a [pull request](https://github.com/ashara12/awesome-deeplearning/pulls).