### 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)[neuraltalk](https://github.com/karpathy/neuraltalk) by Andrej Karpathy : numpy-based RNN/LSTM implementation
5.  [An introduction to genetic algorithms](https://svn-d1.mpi-inf.mpg.de/AG1/MultiCoreLab/papers/ebook-fuzzy-mitchell-99.pdf)
6.  [Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu/)
7.  [Deep Learning in Neural Networks: An Overview](http://arxiv.org/pdf/1404.7828v4.pdf)
 
### 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)
9.  [Deep Learning for Natural Language Processing - Stanford](http://cs224d.stanford.edu/)
10.  [Neural Networks - usherbrooke](http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html)
11.  [Machine Learning - Oxford](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) (2014-2015)
12.  [Deep Learning - Nvidia](https://developer.nvidia.com/deep-learning-courses) (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
13.  [Unsupervised Deep Learning - Stanford](http://web.stanford.edu/class/cs294a/handouts.html) by Andrew Ng in Stanford (2011)
14.  [Natural Language Processing] (http://web.stanford.edu/class/cs224n/handouts/) By Chris Manning in Stanford

### 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/)
5.  [Neural Networks for Named Entity
Recognition](http://nlp.stanford.edu/~socherr/pa4_ner.pdf) [zip](http://nlp.stanford.edu/~socherr/pa4-ner.zip)
6. [Training tricks by YB](http://www.iro.umontreal.ca/~bengioy/papers/YB-tricks.pdf)
7. [Geoff Hinton's reading list (all papers)] (http://www.cs.toronto.edu/~hinton/deeprefs.html)
8. [Supervised Sequence Labelling with Recurrent Neural Networks](http://www.cs.toronto.edu/~graves/preprint.pdf)
9.  [Statistical Language Models based on Neural Networks](http://www.fit.vutbr.cz/~imikolov/rnnlm/thesis.pdf)
10.  [Training Recurrent Neural Networks](http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf)
11.  [Recursive Deep Learning for Natural Language Processing and Computer Vision](http://nlp.stanford.edu/~socherr/thesis.pdf)
12.  [Bi-directional RNN](http://www.di.ufpe.br/~fnj/RNA/bibliografia/BRNN.pdf)
13.  [LSTM](http://web.eecs.utk.edu/~itamar/courses/ECE-692/Bobby_paper1.pdf)
14.  [GRU - Gated Recurrent Unit](http://arxiv.org/pdf/1406.1078v3.pdf)
15.  [GFRNN](http://arxiv.org/pdf/1502.02367v3.pdf) [.](http://jmlr.org/proceedings/papers/v37/chung15.pdf) [.](http://jmlr.org/proceedings/papers/v37/chung15-supp.pdf)
16.  [LSTM: A Search Space Odyssey](http://arxiv.org/pdf/1503.04069v1.pdf)
17.  [A Critical Review of Recurrent Neural Networks for Sequence Learning](http://arxiv.org/pdf/1506.00019v1.pdf)
18.  [Visualizing and Understanding Recurrent Networks](http://arxiv.org/pdf/1506.02078v1.pdf)
19.  [Wojciech Zaremba, Ilya Sutskever, An Empirical Exploration of Recurrent Network Architectures](http://jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
20.  [Recurrent Neural Network based Language Model](http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf)
21.  [Extensions of Recurrent Neural Network Language Model](http://www.fit.vutbr.cz/research/groups/speech/publi/2011/mikolov_icassp2011_5528.pdf)
22.  [Recurrent Neural Network based Language Modeling in Meeting Recognition](http://www.fit.vutbr.cz/~imikolov/rnnlm/ApplicationOfRNNinMeetingRecognition_IS2011.pdf)
23.  [Deep Neural Networks for Acoustic Modeling in Speech Recognition](http://cs224d.stanford.edu/papers/maas_paper.pdf)
24.  [Speech Recognition with Deep Recurrent Neural Networks](http://www.cs.toronto.edu/~fritz/absps/RNN13.pdf)
25.  [Reinforcement Learning Neural Turing Machines](http://arxiv.org/pdf/1505.00521v1)
26.  [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](http://arxiv.org/pdf/1406.1078v3.pdf)
27. [Google - Sequence to Sequence  Learning with Nneural Networks](http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf)
28. [Memory Networks](http://arxiv.org/pdf/1410.3916v10)
29. [Policy Learning with Continuous Memory States for Partially Observed Robotic Control](http://arxiv.org/pdf/1507.01273v1)
30. [Microsoft - Jointly Modeling Embedding and Translation to Bridge Video and Language](http://arxiv.org/pdf/1505.01861v1.pdf)
31. [Neural Turing Machines](http://arxiv.org/pdf/1410.5401v2.pdf)






### 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)
7.  [Neural Networks for Matlab](http://uk.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf)
8.  [Using convolutional neural nets to detect facial keypoints tutorial](http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/)
9.  [Torch7 Tutorials](http://code.madbits.com/wiki/doku.php)
10.  [The Best Machine Learning Tutorials On The Web] (https://github.com/josephmisiti/machine-learning-module)



### WebSites

1.  [deeplearning.net](http://deeplearning.net/)
2.  [deeplearning.stanford.edu](http://deeplearning.stanford.edu/)
3.  [nlp.stanford.edu](http://nlp.stanford.edu/)
4.  [ai-junkie.com](http://www.ai-junkie.com/ann/evolved/nnt1.html)
5.  [cs.brown.edu/research/ai](http://cs.brown.edu/research/ai/)
6.  [eecs.umich.edu/ai](http://www.eecs.umich.edu/ai/)
7.  [cs.utexas.edu/users/ai-lab](http://www.cs.utexas.edu/users/ai-lab/)
8.  [cs.washington.edu/research/ai](http://www.cs.washington.edu/research/ai/)
9.  [aiai.ed.ac.uk](http://www.aiai.ed.ac.uk/)
10.  [www-aig.jpl.nasa.gov](http://www-aig.jpl.nasa.gov/)
11.  [csail.mit.edu](http://www.csail.mit.edu/)
12.  [cgi.cse.unsw.edu.au/~aishare](http://cgi.cse.unsw.edu.au/~aishare/)
13.  [cs.rochester.edu/research/ai](http://www.cs.rochester.edu/research/ai/)
14.  [ai.sri.com](http://www.ai.sri.com/)
15.  [isi.edu/AI/isd.htm](http://www.isi.edu/AI/isd.htm)
16.  [nrl.navy.mil/itd/aic](http://www.nrl.navy.mil/itd/aic/)
17.  [hips.seas.harvard.edu](http://hips.seas.harvard.edu/)
18.  [AI Weekly](http://aiweekly.co)
19.  [stat.ucla.edu](http://www.stat.ucla.edu/~junhua.mao/m-RNN.html)
20.  [deeplearning.cs.toronto.edu](http://deeplearning.cs.toronto.edu/i2t)
21.  [jeffdonahue.com/lrcn/](http://jeffdonahue.com/lrcn/)
22.  [visualqa.org](http://www.visualqa.org/)
23.  [www.mpi-inf.mpg.de/departments/computer-vision...](https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/)
24.  [Deep Learning News](http://news.startup.ml/)

### 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/)
8.  [UC Irvine Machine Learning Repository](http://archive.ics.uci.edu/ml/)
9.  [Flickr 8k](http://nlp.cs.illinois.edu/HockenmaierGroup/Framing_Image_Description/KCCA.html)
10. [Flickr 30k](http://shannon.cs.illinois.edu/DenotationGraph/)
11. [Microsoft COCO](http://mscoco.org/home/)
12. [VQA](http://www.visualqa.org/)
13. [Image QA](http://www.cs.toronto.edu/~mren/imageqa/data/cocoqa/)



### 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)
16.  [cuDNN](https://developer.nvidia.com/cuDNN)
17.  [MGL](http://melisgl.github.io/mgl-pax-world/mgl-manual.html)
18.  [KUnet.jl](https://github.com/denizyuret/KUnet.jl)
19.  [Nvidia DIGITS - a web app based on Caffe](https://github.com/NVIDIA/DIGITS)
20.  [Neon - Python based Deep Learning Framework](https://github.com/NervanaSystems/neon)
21.  [Keras - Theano based Deep Learning Library](http://keras.io)
22.  [Chainer - A flexible framework of neural networks for deep learning](http://chainer.org/)
23.  [RNNLM Toolkit](http://rnnlm.org/)
24.  [RNNLIB - A recurrent neural network library](http://sourceforge.net/p/rnnl/wiki/Home/)
25.  [char-rnn](https://github.com/karpathy/char-rnn)


### 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://github.com/tleyden/docker/tree/master/caffe)
6.  [TorontoDeepLEarning convnet](https://github.com/TorontoDeepLearning/convnet)
7.  [Vision data sets](http://www.cs.cmu.edu/~cil/v-images.html)
8.  [gfx.js](https://github.com/clementfarabet/gfx.js)
9. [Torch7 Cheat sheet](https://github.com/torch/torch7/wiki/Cheatsheet)
10. [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/)
11. [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/)
12. [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/)
13. [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)
14. [Implementing a Distributed Deep Learning Network over Spark](http://www.datasciencecentral.com/profiles/blogs/implementing-a-distributed-deep-learning-network-over-spark)
15. [A chess AI that learns to play chess using deep learning.](https://github.com/erikbern/deep-pink)
16. [Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind] (https://github.com/kristjankorjus/Replicating-DeepMind)
17. [Wiki2Vec. Getting Word2vec vectors for entities and word from Wikipedia Dumps](https://github.com/idio/wiki2vec)
18. [The original code from the DeepMind article + tweaks](https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner)
19. [Google deepdream - Neural Network art](https://github.com/google/deepdream)
20. [An efficient, batched LSTM.](https://gist.github.com/karpathy/587454dc0146a6ae21fc)



-----
### 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). 

-----
## License

[![CC0](http://i.creativecommons.org/p/zero/1.0/88x31.png)](http://creativecommons.org/publicdomain/zero/1.0/)

To the extent possible under law, [Christos Christofidis](https://linkedin.com/in/Christofidis) has waived all copyright and related or neighboring rights to this work.