Free Online Books

1. Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville (01/01/2015)
2. Neural Networks and Deep Learning by Michael Nielsen (Dec 2014)
3. Deep Learning by Microsoft Research (2013)
4. Deep Learning Tutorial by LISA lab, University of Montreal (Jan 6 2015)

Courses

1. Machine Learning - Stanford by Andrew Ng in Coursera (2010-2014)
2. Machine Learning - Caltech by Yaser Abu-Mostafa (2012-2014)
3. Machine Learning - Carnegie Mellon by Tom Mitchell (Spring 2011)
4. Neural Networks for Machine Learning by Geoffrey Hinton in Coursera (2012)
5. Neural networks class by Hugo Larochelle from Université de Sherbrooke (2013)
6. Deep Learning Course by CILVR lab @ NYU (2014)
7. A.I - Berkeley by Dan Klein and Pieter Abbeel (2013)
8. A.I - MIT by Patrick Henry Winston (2010)
9. Vision and learning - computers and brains by Shimon Ullman, Tomaso Poggio, Ethan Meyers @ MIT (2013)

Video and Lectures

1. How To Create A Mind By Ray Kurzweil
2. Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng
3. Recent Developments in Deep Learning By Geoff Hinton
4. The Unreasonable Effectiveness of Deep Learning by Yann LeCun
5. Deep Learning of Representations by Yoshua bengio
6. Principles of Hierarchical Temporal Memory by Jeff Hawkins
7. Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab by Adam Coates
8. Making Sense of the World with Deep Learning By Adam Coates
9. Demystifying Unsupervised Feature Learning By Adam Coates
10. Visual Perception with Deep Learning By Yann LeCun
11. The Next Generation of Neural Networks By Geoffrey Hinton at GoogleTechTalks
12. The wonderful and terrifying implications of computers that can learn By Jeremy Howard at TEDxBrussels

Papers

1. ImageNet Classification with Deep Convolutional Neural Networks
2. Using Very Deep Autoencoders for Content Based Image Retrieval
3. Learning Deep Architectures for AI
4. CMU’s list of papers

Tutorials

1. UFLDL Tutorial 1
2. UFLDL Tutorial 2
3. Deep Learning for NLP (without Magic)
4. A Deep Learning Tutorial: From Perceptrons to Deep Networks
5. Deep Learning from the Bottom up

WebSites

1. deeplearning.net
2. deeplearning.stanford.edu

Datasets

1. MNIST Handwritten digits
2. Google House Numbers from street view
3. CIFAR-10 and CIFAR-1004.
4. IMAGENET
5. Tiny Images 80 Million tiny images6.
6. Flickr Data 100 Million Yahoo dataset
7. Berkeley Segmentation Dataset 500

Frameworks

1. Caffe
2. Torch7
3. Theano
4. cuda-convnet
5. Ccv
6. NuPIC
7. DeepLearning4J
8. Brain

Miscellaneous

1. Google Plus - Deep Learning Community
2. Caffe Webinar
3. 100 Best Github Resources in Github for DL
4. Word2Vec
5. Caffe DockerFile
6. TorontoDeepLEarning convnet
7. Vision data sets
8. Fantastic Torch Tutorial
9. gfx.js
10. Torch7 Cheat sheet
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
13. Misc from MIT's 'Networks for Learning: Regression and Classification' course
14. Misc from MIT's 'Neural Coding and Perception of Sound' course
15. Implementing a Distributed Deep Learning Network over Spark

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Contributing

Have anything in mind that you think is awesome and would fit in this list? Feel free to send a pull request.