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Title: GitHub - python2014/awesome-deep-learning: A curated list of awesome Deep Learning tutorials, projects and communities.

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Deep Learninghttp://www.iro.umontreal.ca/~bengioy/dlbook/
Neural Networks and Deep Learninghttp://neuralnetworksanddeeplearning.com/
Deep Learninghttp://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf
Deep Learning Tutorialhttp://deeplearning.net/tutorial/deeplearning.pdf
neuraltalkhttps://github.com/karpathy/neuraltalk
An introduction to genetic algorithmshttps://svn-d1.mpi-inf.mpg.de/AG1/MultiCoreLab/papers/ebook-fuzzy-mitchell-99.pdf
Artificial Intelligence: A Modern Approachhttp://aima.cs.berkeley.edu/
Deep Learning in Neural Networks: An Overviewhttp://arxiv.org/pdf/1404.7828v4.pdf
https://patch-diff.githubusercontent.com/python2014/awesome-deep-learning#courses
Machine Learning - Stanfordhttps://class.coursera.org/ml-005
Machine Learning - Caltechhttp://work.caltech.edu/lectures.html
Machine Learning - Carnegie Mellonhttp://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml
Neural Networks for Machine Learninghttps://class.coursera.org/neuralnets-2012-001
Neural networks classhttps://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH
Deep Learning Coursehttp://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start
A.I - Berkeleyhttps://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/courseware/
A.I - MIThttp://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/
Vision and learning - computers and brainshttp://web.mit.edu/course/other/i2course/www/vision_and_learning_fall_2013.html
Convolutional Neural Networks for Visual Recognition - Stanfordhttp://vision.stanford.edu/teaching/cs231n/syllabus_winter2015.html
Convolutional Neural Networks for Visual Recognition - Stanfordhttp://vision.stanford.edu/teaching/cs231n/syllabus.html
Deep Learning for Natural Language Processing - Stanfordhttp://cs224d.stanford.edu/
Neural Networks - usherbrookehttp://info.usherbrooke.ca/hlarochelle/neural_networks/content.html
Machine Learning - Oxfordhttps://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
Deep Learning - Nvidiahttps://developer.nvidia.com/deep-learning-courses
Graduate Summer School: Deep Learning, Feature Learninghttps://www.youtube.com/playlist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA
Deep Learning - Udacity/Googlehttps://www.udacity.com/course/deep-learning--ud730
Deep Learning - UWaterloohttps://www.youtube.com/playlist?list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE
Statistical Machine Learning - CMUhttps://www.youtube.com/watch?v=azaLcvuql_g&list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r
Deep Learning Coursehttps://www.college-de-france.fr/site/en-yann-lecun/course-2015-2016.htm
Bay area DL schoolhttp://www.bayareadlschool.org/
Designing, Visualizing and Understanding Deep Neural Networks-UC Berkeleyhttps://www.youtube.com/playlist?list=PLkFD6_40KJIxopmdJF_CLNqG3QuDFHQUm
UVA Deep Learning Coursehttp://uvadlc.github.io
MIT 6.S094: Deep Learning for Self-Driving Carshttp://selfdrivingcars.mit.edu/
MIT 6.S191: Introduction to Deep Learninghttp://introtodeeplearning.com/
Berkeley CS 294: Deep Reinforcement Learninghttp://rll.berkeley.edu/deeprlcourse/
Keras in Motion video coursehttps://www.manning.com/livevideo/keras-in-motion
Practical Deep Learning For Codershttp://course.fast.ai/
https://patch-diff.githubusercontent.com/python2014/awesome-deep-learning#videos-and-lectures
How To Create A Mindhttps://www.youtube.com/watch?v=RIkxVci-R4k
Deep Learning, Self-Taught Learning and Unsupervised Feature Learninghttps://www.youtube.com/watch?v=n1ViNeWhC24
Recent Developments in Deep Learninghttps://www.youtube.com/watch?v=vShMxxqtDDs&index=3&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT
The Unreasonable Effectiveness of Deep Learninghttps://www.youtube.com/watch?v=sc-KbuZqGkI
Deep Learning of Representationshttps://www.youtube.com/watch?v=4xsVFLnHC_0
Principles of Hierarchical Temporal Memoryhttps://www.youtube.com/watch?v=6ufPpZDmPKA
Machine Learning Discussion Group - Deep Learning w/ Stanford AI Labhttps://www.youtube.com/watch?v=2QJi0ArLq7s&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT
Making Sense of the World with Deep Learninghttp://vimeo.com/80821560
Demystifying Unsupervised Feature Learning https://www.youtube.com/watch?v=wZfVBwOO0-k
Visual Perception with Deep Learninghttps://www.youtube.com/watch?v=3boKlkPBckA
The Next Generation of Neural Networkshttps://www.youtube.com/watch?v=AyzOUbkUf3M
The wonderful and terrifying implications of computers that can learnhttp://www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn
Unsupervised Deep Learning - Stanfordhttp://web.stanford.edu/class/cs294a/handouts.html
Natural Language Processinghttp://web.stanford.edu/class/cs224n/handouts/
A beginners Guide to Deep Neural Networkshttp://googleresearch.blogspot.com/2015/09/a-beginners-guide-to-deep-neural.html
Deep Learning: Intelligence from Big Datahttps://www.youtube.com/watch?v=czLI3oLDe8M
Introduction to Artificial Neural Networks and Deep Learninghttps://www.youtube.com/watch?v=FoO8qDB8gUU
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ImageNet Classification with Deep Convolutional Neural Networkshttp://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
Using Very Deep Autoencoders for Content Based Image Retrievalhttp://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf
Learning Deep Architectures for AIhttp://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf
CMU’s list of papershttp://deeplearning.cs.cmu.edu/
Neural Networks for Named Entity Recognitionhttp://nlp.stanford.edu/~socherr/pa4_ner.pdf
ziphttp://nlp.stanford.edu/~socherr/pa4-ner.zip
Training tricks by YBhttp://www.iro.umontreal.ca/~bengioy/papers/YB-tricks.pdf
Geoff Hinton's reading list (all papers)http://www.cs.toronto.edu/~hinton/deeprefs.html
Supervised Sequence Labelling with Recurrent Neural Networkshttp://www.cs.toronto.edu/~graves/preprint.pdf
Statistical Language Models based on Neural Networkshttp://www.fit.vutbr.cz/~imikolov/rnnlm/thesis.pdf
Training Recurrent Neural Networkshttp://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
Recursive Deep Learning for Natural Language Processing and Computer Visionhttp://nlp.stanford.edu/~socherr/thesis.pdf
Bi-directional RNNhttp://www.di.ufpe.br/~fnj/RNA/bibliografia/BRNN.pdf
LSTMhttp://web.eecs.utk.edu/~itamar/courses/ECE-692/Bobby_paper1.pdf
GRU - Gated Recurrent Unithttp://arxiv.org/pdf/1406.1078v3.pdf
GFRNNhttp://arxiv.org/pdf/1502.02367v3.pdf
.http://jmlr.org/proceedings/papers/v37/chung15.pdf
.http://jmlr.org/proceedings/papers/v37/chung15-supp.pdf
LSTM: A Search Space Odysseyhttp://arxiv.org/pdf/1503.04069v1.pdf
A Critical Review of Recurrent Neural Networks for Sequence Learninghttp://arxiv.org/pdf/1506.00019v1.pdf
Visualizing and Understanding Recurrent Networkshttp://arxiv.org/pdf/1506.02078v1.pdf
Wojciech Zaremba, Ilya Sutskever, An Empirical Exploration of Recurrent Network Architectureshttp://jmlr.org/proceedings/papers/v37/jozefowicz15.pdf
Recurrent Neural Network based Language Modelhttp://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf
Extensions of Recurrent Neural Network Language Modelhttp://www.fit.vutbr.cz/research/groups/speech/publi/2011/mikolov_icassp2011_5528.pdf
Recurrent Neural Network based Language Modeling in Meeting Recognitionhttp://www.fit.vutbr.cz/~imikolov/rnnlm/ApplicationOfRNNinMeetingRecognition_IS2011.pdf
Deep Neural Networks for Acoustic Modeling in Speech Recognitionhttp://cs224d.stanford.edu/papers/maas_paper.pdf
Speech Recognition with Deep Recurrent Neural Networkshttp://www.cs.toronto.edu/~fritz/absps/RNN13.pdf
Reinforcement Learning Neural Turing Machineshttp://arxiv.org/pdf/1505.00521v1
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationhttp://arxiv.org/pdf/1406.1078v3.pdf
Google - Sequence to Sequence Learning with Neural Networkshttp://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf
Memory Networkshttp://arxiv.org/pdf/1410.3916v10
Policy Learning with Continuous Memory States for Partially Observed Robotic Controlhttp://arxiv.org/pdf/1507.01273v1
Microsoft - Jointly Modeling Embedding and Translation to Bridge Video and Languagehttp://arxiv.org/pdf/1505.01861v1.pdf
Neural Turing Machineshttp://arxiv.org/pdf/1410.5401v2.pdf
Ask Me Anything: Dynamic Memory Networks for Natural Language Processinghttp://arxiv.org/pdf/1506.07285v1.pdf
Mastering the Game of Go with Deep Neural Networks and Tree Searchhttp://www.nature.com/nature/journal/v529/n7587/pdf/nature16961.pdf
Batch Normalizationhttps://arxiv.org/abs/1502.03167
Residual Learninghttps://arxiv.org/pdf/1512.03385v1.pdf
Image-to-Image Translation with Conditional Adversarial Networkshttps://arxiv.org/pdf/1611.07004v1.pdf
Berkeley AI Research (BAIR) Laboratoryhttps://arxiv.org/pdf/1611.07004v1.pdf
MobileNets by Googlehttps://arxiv.org/abs/1704.04861
Cross Audio-Visual Recognition in the Wild Using Deep Learninghttps://arxiv.org/abs/1706.05739
https://patch-diff.githubusercontent.com/python2014/awesome-deep-learning#tutorials
UFLDL Tutorial 1http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial
UFLDL Tutorial 2http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/
Deep Learning for NLP (without Magic)http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
A Deep Learning Tutorial: From Perceptrons to Deep Networkshttp://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks
Deep Learning from the Bottom uphttp://www.metacademy.org/roadmaps/rgrosse/deep_learning
Theano Tutorialhttp://deeplearning.net/tutorial/deeplearning.pdf
Neural Networks for Matlabhttp://uk.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf
Using convolutional neural nets to detect facial keypoints tutorialhttp://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/
Torch7 Tutorialshttps://github.com/clementfarabet/ipam-tutorials/tree/master/th_tutorials
The Best Machine Learning Tutorials On The Webhttps://github.com/josephmisiti/machine-learning-module
VGG Convolutional Neural Networks Practicalhttp://www.robots.ox.ac.uk/~vgg/practicals/cnn/index.html
TensorFlow tutorialshttps://github.com/nlintz/TensorFlow-Tutorials
More TensorFlow tutorialshttps://github.com/pkmital/tensorflow_tutorials
TensorFlow Python Notebookshttps://github.com/aymericdamien/TensorFlow-Examples
Keras and Lasagne Deep Learning Tutorialshttps://github.com/Vict0rSch/deep_learning
Classification on raw time series in TensorFlow with a LSTM RNNhttps://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition
Using convolutional neural nets to detect facial keypoints tutorialhttp://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/
TensorFlow-Worldhttps://github.com/astorfi/TensorFlow-World
https://patch-diff.githubusercontent.com/python2014/awesome-deep-learning#researchers
Aaron Courvillehttp://aaroncourville.wordpress.com
Abdel-rahman Mohamedhttp://www.cs.toronto.edu/~asamir/
Adam Coateshttp://cs.stanford.edu/~acoates/
Alex Acerohttp://research.microsoft.com/en-us/people/alexac/
Alex Krizhevsky http://www.cs.utoronto.ca/~kriz/index.html
Alexander Ilin http://users.ics.aalto.fi/alexilin/
Amos Storkey http://homepages.inf.ed.ac.uk/amos/
Andrej Karpathy http://cs.stanford.edu/~karpathy/
Andrew M. Saxe http://www.stanford.edu/~asaxe/
Andrew Ng http://www.cs.stanford.edu/people/ang/
Andrew W. Senior http://research.google.com/pubs/author37792.html
Andriy Mnih http://www.gatsby.ucl.ac.uk/~amnih/
Ayse Naz Erkan http://www.cs.nyu.edu/~naz/
Benjamin Schrauwen http://reslab.elis.ugent.be/benjamin
Bernardete Ribeiro https://www.cisuc.uc.pt/people/show/2020
Bo David Chen http://vision.caltech.edu/~bchen3/Site/Bo_David_Chen.html
Boureau Y-Lan http://cs.nyu.edu/~ylan/
Brian Kingsbury http://researcher.watson.ibm.com/researcher/view.php?person=us-bedk
Christopher Manning http://nlp.stanford.edu/~manning/
Clement Farabet http://www.clement.farabet.net/
Dan Claudiu Cireșan http://www.idsia.ch/~ciresan/
David Reichert http://serre-lab.clps.brown.edu/person/david-reichert/
Derek Rose http://mil.engr.utk.edu/nmil/member/5.html
Dong Yu http://research.microsoft.com/en-us/people/dongyu/default.aspx
Drausin Wulsin http://www.seas.upenn.edu/~wulsin/
Erik M. Schmidt http://music.ece.drexel.edu/people/eschmidt
Eugenio Culurciello https://engineering.purdue.edu/BME/People/viewPersonById?resource_id=71333
Frank Seide http://research.microsoft.com/en-us/people/fseide/
Galen Andrew http://homes.cs.washington.edu/~galen/
Geoffrey Hinton http://www.cs.toronto.edu/~hinton/
George Dahl http://www.cs.toronto.edu/~gdahl/
Graham Taylor http://www.uoguelph.ca/~gwtaylor/
Grégoire Montavon http://gregoire.montavon.name/
Guido Francisco Montúfar http://personal-homepages.mis.mpg.de/montufar/
Guillaume Desjardins http://brainlogging.wordpress.com/
Hannes Schulz http://www.ais.uni-bonn.de/~schulz/
Hélène Paugam-Moisy http://www.lri.fr/~hpaugam/
Honglak Lee http://web.eecs.umich.edu/~honglak/
Hugo Larochelle http://www.dmi.usherb.ca/~larocheh/index_en.html
Ilya Sutskever http://www.cs.toronto.edu/~ilya/
Itamar Arel http://mil.engr.utk.edu/nmil/member/2.html
James Martens http://www.cs.toronto.edu/~jmartens/
Jason Morton http://www.jasonmorton.com/
Jason Weston http://www.thespermwhale.com/jaseweston/
Jeff Dean http://research.google.com/pubs/jeff.html
Jiquan Mgiam http://cs.stanford.edu/~jngiam/
Joseph Turian http://www-etud.iro.umontreal.ca/~turian/
Joshua Matthew Susskind http://aclab.ca/users/josh/index.html
Jürgen Schmidhuber http://www.idsia.ch/~juergen/
Justin A. Blanco https://sites.google.com/site/blancousna/
Koray Kavukcuoglu http://koray.kavukcuoglu.org/
KyungHyun Cho http://users.ics.aalto.fi/kcho/
Li Deng http://research.microsoft.com/en-us/people/deng/
Lucas Theis http://www.kyb.tuebingen.mpg.de/nc/employee/details/lucas.html
Ludovic Arnold http://ludovicarnold.altervista.org/home/
Marc'Aurelio Ranzato http://www.cs.nyu.edu/~ranzato/
Martin Längkvist http://aass.oru.se/~mlt/
Misha Denil http://mdenil.com/
Mohammad Norouzi http://www.cs.toronto.edu/~norouzi/
Nando de Freitas http://www.cs.ubc.ca/~nando/
Navdeep Jaitly http://www.cs.utoronto.ca/~ndjaitly/
Nicolas Le Roux http://nicolas.le-roux.name/
Nitish Srivastava http://www.cs.toronto.edu/~nitish/
Noel Lopes https://www.cisuc.uc.pt/people/show/2028
Oriol Vinyals http://www.cs.berkeley.edu/~vinyals/
Pascal Vincent http://www.iro.umontreal.ca/~vincentp
Patrick Nguyen https://sites.google.com/site/drpngx/
Pedro Domingos http://homes.cs.washington.edu/~pedrod/
Peggy Series http://homepages.inf.ed.ac.uk/pseries/
Pierre Sermanet http://cs.nyu.edu/~sermanet
Piotr Mirowski http://www.cs.nyu.edu/~mirowski/
Quoc V. Le http://ai.stanford.edu/~quocle/
Reinhold Scherer http://bci.tugraz.at/scherer/
Richard Socher http://www.socher.org/
Rob Fergus http://cs.nyu.edu/~fergus/pmwiki/pmwiki.php
Robert Coop http://mil.engr.utk.edu/nmil/member/19.html
Robert Gens http://homes.cs.washington.edu/~rcg/
Roger Grosse http://people.csail.mit.edu/rgrosse/
Ronan Collobert http://ronan.collobert.com/
Ruslan Salakhutdinov http://www.utstat.toronto.edu/~rsalakhu/
Sebastian Gerwinn http://www.kyb.tuebingen.mpg.de/nc/employee/details/sgerwinn.html
Stéphane Mallat http://www.cmap.polytechnique.fr/~mallat/
Sven Behnke http://www.ais.uni-bonn.de/behnke/
Tapani Raiko http://users.ics.aalto.fi/praiko/
Tara Sainath https://sites.google.com/site/tsainath/
Tijmen Tieleman http://www.cs.toronto.edu/~tijmen/
Tom Karnowski http://mil.engr.utk.edu/nmil/member/36.html
Tomáš Mikolov https://research.facebook.com/tomas-mikolov
Ueli Meier http://www.idsia.ch/~meier/
Vincent Vanhoucke http://vincent.vanhoucke.com
Volodymyr Mnih http://www.cs.toronto.edu/~vmnih/
Yann LeCun http://yann.lecun.com/
Yichuan Tang http://www.cs.toronto.edu/~tang/
Yoshua Bengio http://www.iro.umontreal.ca/~bengioy/yoshua_en/index.html
Yotaro Kubo http://yota.ro/
Youzhi (Will) Zou http://ai.stanford.edu/~wzou
Fei-Fei Li http://vision.stanford.edu/feifeili
Ian Goodfellow https://research.google.com/pubs/105214.html
Robert Laganière http://www.site.uottawa.ca/~laganier/
https://patch-diff.githubusercontent.com/python2014/awesome-deep-learning#websites
deeplearning.nethttp://deeplearning.net/
deeplearning.stanford.eduhttp://deeplearning.stanford.edu/
nlp.stanford.eduhttp://nlp.stanford.edu/
ai-junkie.comhttp://www.ai-junkie.com/ann/evolved/nnt1.html
cs.brown.edu/research/aihttp://cs.brown.edu/research/ai/
eecs.umich.edu/aihttp://www.eecs.umich.edu/ai/
cs.utexas.edu/users/ai-labhttp://www.cs.utexas.edu/users/ai-lab/
cs.washington.edu/research/aihttp://www.cs.washington.edu/research/ai/
aiai.ed.ac.ukhttp://www.aiai.ed.ac.uk/
www-aig.jpl.nasa.govhttp://www-aig.jpl.nasa.gov/
csail.mit.eduhttp://www.csail.mit.edu/
cgi.cse.unsw.edu.au/~aisharehttp://cgi.cse.unsw.edu.au/~aishare/
cs.rochester.edu/research/aihttp://www.cs.rochester.edu/research/ai/
ai.sri.comhttp://www.ai.sri.com/
isi.edu/AI/isd.htmhttp://www.isi.edu/AI/isd.htm
nrl.navy.mil/itd/aichttp://www.nrl.navy.mil/itd/aic/
hips.seas.harvard.eduhttp://hips.seas.harvard.edu/
AI Weeklyhttp://aiweekly.co
stat.ucla.eduhttp://www.stat.ucla.edu/~junhua.mao/m-RNN.html
deeplearning.cs.toronto.eduhttp://deeplearning.cs.toronto.edu/i2t
jeffdonahue.com/lrcn/http://jeffdonahue.com/lrcn/
visualqa.orghttp://www.visualqa.org/
www.mpi-inf.mpg.de/departments/computer-vision...https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/
Deep Learning Newshttp://news.startup.ml/
Machine Learning is Fun! Adam Geitgey's Bloghttps://medium.com/@ageitgey/
https://patch-diff.githubusercontent.com/python2014/awesome-deep-learning#datasets
MNISThttp://yann.lecun.com/exdb/mnist/
Google House Numbershttp://ufldl.stanford.edu/housenumbers/
CIFAR-10 and CIFAR-100http://www.cs.toronto.edu/~kriz/cifar.html
IMAGENEThttp://www.image-net.org/
Tiny Imageshttp://groups.csail.mit.edu/vision/TinyImages/
Flickr Datahttps://yahooresearch.tumblr.com/post/89783581601/one-hundred-million-creative-commons-flickr-images
Berkeley Segmentation Dataset 500http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
UC Irvine Machine Learning Repositoryhttp://archive.ics.uci.edu/ml/
Flickr 8khttp://nlp.cs.illinois.edu/HockenmaierGroup/Framing_Image_Description/KCCA.html
Flickr 30khttp://shannon.cs.illinois.edu/DenotationGraph/
Microsoft COCOhttp://mscoco.org/home/
VQAhttp://www.visualqa.org/
Image QAhttp://www.cs.toronto.edu/~mren/imageqa/data/cocoqa/
AT&T Laboratories Cambridge face databasehttp://www.uk.research.att.com/facedatabase.html
AVHRR Pathfinderhttp://xtreme.gsfc.nasa.gov
Air Freighthttp://www.anc.ed.ac.uk/~amos/afreightdata.html
Amsterdam Library of Object Imageshttp://www.science.uva.nl/~aloi/
Annotated face, hand, cardiac & meat imageshttp://www.imm.dtu.dk/~aam/
Image Analysis and Computer Graphicshttp://www.imm.dtu.dk/image/
Brown University Stimulihttp://www.cog.brown.edu/~tarr/stimuli.html
CAVIAR video sequences of mall and public space behaviorhttp://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/
Machine Vision Unithttp://www.ipab.inf.ed.ac.uk/mvu/
CCITT Fax standard imageshttp://www.cs.waikato.ac.nz/~singlis/ccitt.html
CMU CIL's Stereo Data with Ground Truthhttps://patch-diff.githubusercontent.com/python2014/awesome-deep-learning/blob/master/cil-ster.html
CMU PIE Databasehttp://www.ri.cmu.edu/projects/project_418.html
CMU VASC Image Databasehttp://www.ius.cs.cmu.edu/idb/
Caltech Image Databasehttp://www.vision.caltech.edu/html-files/archive.html
Columbia-Utrecht Reflectance and Texture Databasehttp://www.cs.columbia.edu/CAVE/curet/
Computational Colour Constancy Datahttp://www.cs.sfu.ca/~colour/data/index.html
Computational Vision Labhttp://www.cs.sfu.ca/~colour/
Content-based image retrieval databasehttp://www.cs.washington.edu/research/imagedatabase/groundtruth/
Efficient Content-based Retrieval Grouphttp://www.cs.washington.edu/research/imagedatabase/
Densely Sampled View Sphereshttp://ls7-www.cs.uni-dortmund.de/~peters/pages/research/modeladaptsys/modeladaptsys_vba_rov.html
Computer Science VII (Graphical Systems)http://ls7-www.cs.uni-dortmund.de/
Digital Embryoshttps://web-beta.archive.org/web/20011216051535/vision.psych.umn.edu/www/kersten-lab/demos/digitalembryo.html
Univerity of Minnesota Vision Labhttp://vision.psych.umn.edu/www/kersten-lab/kersten-lab.html
El Salvador Atlas of Gastrointestinal VideoEndoscopyhttp://www.gastrointestinalatlas.com
FG-NET Facial Aging Databasehttp://sting.cycollege.ac.cy/~alanitis/fgnetaging/index.htm
FVC2000 Fingerprint Databaseshttp://bias.csr.unibo.it/fvc2000/
Biometric Systems Labhttp://bias.csr.unibo.it/research/biolab
Face and Gesture images and image sequenceshttp://www.fg-net.org
German Fingerspelling Databasehttp://www-i6.informatik.rwth-aachen.de/~dreuw/database.html
Language Processing and Pattern Recognitionhttp://www-i6.informatik.rwth-aachen.de/
Groningen Natural Image Databasehttp://hlab.phys.rug.nl/archive.html
ICG Testhouse sequencehttp://www.icg.tu-graz.ac.at/~schindler/Data
Institute of Computer Graphics and Visionhttp://www.icg.tu-graz.ac.at
IEN Image Libraryhttp://www.ien.it/is/vislib/
INRIA's Syntim images databasehttp://www-rocq.inria.fr/~tarel/syntim/images.html
INRIAhttp://www.inria.fr/
INRIA's Syntim stereo databaseshttp://www-rocq.inria.fr/~tarel/syntim/paires.html
Image Analysis Laboratoryhttp://www.ece.ncsu.edu/imaging/Archives/ImageDataBase/index.html
Image Analysis Laboratoryhttp://www.ece.ncsu.edu/imaging
Image Databasehttp://www.prip.tuwien.ac.at/prip/image.html
JAFFE Facial Expression Image Databasehttp://www.mis.atr.co.jp/~mlyons/jaffe.html
ATR Research, Kyoto, Japanhttp://www.mic.atr.co.jp/
MIT Vision Texturehttp://www-white.media.mit.edu/vismod/imagery/VisionTexture/vistex.html
Machine Visionhttp://vision.cse.psu.edu/book/testbed/images/
Mammography Image Databaseshttp://marathon.csee.usf.edu/Mammography/Database.html
Middlebury Stereo Data Sets with Ground Truthhttp://www.middlebury.edu/stereo/data.html
Middlebury Stereo Vision Research Pagehttp://www.middlebury.edu/stereo
Modis Airborne simulator, Gallery and data sethttp://ltpwww.gsfc.nasa.gov/MODIS/MAS/
NLM HyperDoc Visible Human Projecthttp://www.nlm.nih.gov/research/visible/visible_human.html
National Design Repositoryhttp://www.designrepository.org
Geometric & Intelligent Computing Laboratoryhttp://gicl.mcs.drexel.edu
OSU (MSU) 3D Object Model Databasehttp://eewww.eng.ohio-state.edu/~flynn/3DDB/Models/
OSU (MSU/WSU) Range Image Databasehttp://eewww.eng.ohio-state.edu/~flynn/3DDB/RID/
OSU/SAMPL Database: Range Images, 3D Models, Stills, Motion Sequenceshttp://sampl.eng.ohio-state.edu/~sampl/database.htm
Signal Analysis and Machine Perception Laboratoryhttp://sampl.eng.ohio-state.edu
Otago Optical Flow Evaluation Sequenceshttp://www.cs.otago.ac.nz/research/vision/Research/OpticalFlow/opticalflow.html
Vision Research Grouphttp://www.cs.otago.ac.nz/research/vision/index.html
LIMSI-CNRS/CHM/IMM/visionhttp://www.limsi.fr/Recherche/IMM/PageIMM.html
LIMSI-CNRShttp://www.limsi.fr/
Photometric 3D Surface Texture Databasehttp://www.taurusstudio.net/research/pmtexdb/index.htm
SEQUENCES FOR OPTICAL FLOW ANALYSIS (SOFA)http://www.cee.hw.ac.uk/~mtc/sofa
Computer Vision Grouphttp://www.cee.hw.ac.uk/~mtc/research.html
Sequences for Flow Based Reconstructionhttp://www.nada.kth.se/~zucch/CAMERA/PUB/seq.html
Stereo Images with Ground Truth Disparity and Occlusionhttp://www-dbv.cs.uni-bonn.de/stereo_data/
Stuttgart Range Image Databasehttp://range.informatik.uni-stuttgart.de
Department Image Understandinghttp://www.informatik.uni-stuttgart.de/ipvr/bv/bv_home_engl.html
The AR Face Databasehttp://www2.ece.ohio-state.edu/~aleix/ARdatabase.html
Purdue Robot Vision Labhttp://rvl.www.ecn.purdue.edu/RVL/
The MIT-CSAIL Database of Objects and Sceneshttp://web.mit.edu/torralba/www/database.html
The RVL SPEC-DB (SPECularity DataBase)http://rvl1.ecn.purdue.edu/RVL/specularity_database/
Robot Vision Laboratoryhttp://rvl1.ecn.purdue.edu/RVL/
The Xm2vts databasehttp://xm2vtsdb.ee.surrey.ac.uk
Centre for Vision, Speech and Signal Processinghttp://www.ee.surrey.ac.uk/Research/CVSSP
Traffic Image Sequences and 'Marbled Block' Sequencehttp://i21www.ira.uka.de/image_sequences
IAKS/KOGShttp://i21www.ira.uka.de
U Oulu wood and knots databasehttp://www.ee.oulu.fi/~olli/Projects/Lumber.Grading.html
UCID - an Uncompressed Colour Image Databasehttp://vision.doc.ntu.ac.uk/datasets/UCID/ucid.html
UMass Vision Image Archivehttp://vis-www.cs.umass.edu/~vislib/
USF Range Image Data with Segmentation Ground Truthhttp://marathon.csee.usf.edu/range/seg-comp/SegComp.html
University of Oulu Physics-based Face Databasehttp://www.ee.oulu.fi/research/imag/color/pbfd.html
Machine Vision and Media Processing Unithttp://www.ee.oulu.fi/mvmp/
University of Oulu Texture Databasehttp://www.outex.oulu.fi
Machine Vision Grouphttp://www.ee.oulu.fi/mvg
View Sphere Databasehttp://www-prima.inrialpes.fr/Prima/hall/view_sphere.html
PRIMA, GRAVIRhttp://www-prima.inrialpes.fr/Prima/
Wiry Object Recognition Databasehttp://www.cs.cmu.edu/~owenc/word.htm
3D Vision Grouphttp://www.cs.cmu.edu/0.000000E+003dvision/
Yale Face Databasehttp://cvc.yale.edu/projects/yalefaces/yalefaces.html
Yale Face Database Bhttp://cvc.yale.edu/projects/yalefacesB/yalefacesB.html
Center for Computational Vision and Controlhttp://cvc.yale.edu/
DeepMind QA Corpushttps://github.com/deepmind/rc-data
Paperhttp://arxiv.org/abs/1506.03340
YouTube-8M Datasethttps://research.google.com/youtube8m/
Open Images datasethttps://github.com/openimages/dataset
https://patch-diff.githubusercontent.com/python2014/awesome-deep-learning#frameworks
Caffehttp://caffe.berkeleyvision.org/
Torch7http://torch.ch/
Theanohttp://deeplearning.net/software/theano/
cuda-convnethttps://code.google.com/p/cuda-convnet2/
convetjshttps://github.com/karpathy/convnetjs
Ccvhttp://libccv.org/doc/doc-convnet/
NuPIChttp://numenta.org/nupic.html
DeepLearning4Jhttp://deeplearning4j.org/
Brainhttps://github.com/harthur/brain
DeepLearnToolboxhttps://github.com/rasmusbergpalm/DeepLearnToolbox
Deepnethttps://github.com/nitishsrivastava/deepnet
Deeppyhttps://github.com/andersbll/deeppy
JavaNNhttps://github.com/ivan-vasilev/neuralnetworks
hebelhttps://github.com/hannes-brt/hebel
Mocha.jlhttps://github.com/pluskid/Mocha.jl
OpenDLhttps://github.com/guoding83128/OpenDL
cuDNNhttps://developer.nvidia.com/cuDNN
MGLhttp://melisgl.github.io/mgl-pax-world/mgl-manual.html
Knet.jlhttps://github.com/denizyuret/Knet.jl
Nvidia DIGITS - a web app based on Caffehttps://github.com/NVIDIA/DIGITS
Neon - Python based Deep Learning Frameworkhttps://github.com/NervanaSystems/neon
Keras - Theano based Deep Learning Libraryhttp://keras.io
Chainer - A flexible framework of neural networks for deep learninghttp://chainer.org/
RNNLM Toolkithttp://rnnlm.org/
RNNLIB - A recurrent neural network libraryhttp://sourceforge.net/p/rnnl/wiki/Home/
char-rnnhttps://github.com/karpathy/char-rnn
MatConvNet: CNNs for MATLABhttps://github.com/vlfeat/matconvnet
Minerva - a fast and flexible tool for deep learning on multi-GPUhttps://github.com/dmlc/minerva
Brainstorm - Fast, flexible and fun neural networks.https://github.com/IDSIA/brainstorm
Tensorflow - Open source software library for numerical computation using data flow graphshttps://github.com/tensorflow/tensorflow
DMTK - Microsoft Distributed Machine Learning Tookithttps://github.com/Microsoft/DMTK
Scikit Flow - Simplified interface for TensorFlow (mimicking Scikit Learn)https://github.com/google/skflow
MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning frameworkhttps://github.com/dmlc/mxnet/
Veles - Samsung Distributed machine learning platformhttps://github.com/Samsung/veles
Marvin - A Minimalist GPU-only N-Dimensional ConvNets Frameworkhttps://github.com/PrincetonVision/marvin
Apache SINGA - A General Distributed Deep Learning Platformhttp://singa.incubator.apache.org/
DSSTNE - Amazon's library for building Deep Learning modelshttps://github.com/amznlabs/amazon-dsstne
SyntaxNet - Google's syntactic parser - A TensorFlow dependency libraryhttps://github.com/tensorflow/models/tree/master/syntaxnet
mlpack - A scalable Machine Learning libraryhttp://mlpack.org/
Torchnet - Torch based Deep Learning Libraryhttps://github.com/torchnet/torchnet
Paddle - PArallel Distributed Deep LEarning by Baiduhttps://github.com/baidu/paddle
NeuPy - Theano based Python library for ANN and Deep Learninghttp://neupy.com
Lasagne - a lightweight library to build and train neural networks in Theanohttps://github.com/Lasagne/Lasagne
nolearn - wrappers and abstractions around existing neural network libraries, most notably Lasagnehttps://github.com/dnouri/nolearn
Sonnet - a library for constructing neural networks by Google's DeepMindhttps://github.com/deepmind/sonnet
PyTorch - Tensors and Dynamic neural networks in Python with strong GPU accelerationhttps://github.com/pytorch/pytorch
CNTK - Microsoft Cognitive Toolkithttps://github.com/Microsoft/CNTK
https://patch-diff.githubusercontent.com/python2014/awesome-deep-learning#miscellaneous
Google Plus - Deep Learning Communityhttps://plus.google.com/communities/112866381580457264725
Caffe Webinarhttp://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.php?searchByKeyword=shelhamer&searchItems=&sessionTopic=&sessionEvent=4&sessionYear=2014&sessionFormat=&submit=&select=+
100 Best Github Resources in Github for DLhttp://meta-guide.com/software-meta-guide/100-best-github-deep-learning/
Word2Vechttps://code.google.com/p/word2vec/
Caffe DockerFilehttps://github.com/tleyden/docker/tree/master/caffe
TorontoDeepLEarning convnethttps://github.com/TorontoDeepLearning/convnet
gfx.jshttps://github.com/clementfarabet/gfx.js
Torch7 Cheat sheethttps://github.com/torch/torch7/wiki/Cheatsheet
Misc from MIT's 'Advanced Natural Language Processing' coursehttp://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-864-advanced-natural-language-processing-fall-2005/
Misc from MIT's 'Machine Learning' coursehttp://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/
Misc from MIT's 'Networks for Learning: Regression and Classification' coursehttp://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-520-a-networks-for-learning-regression-and-classification-spring-2001/
Misc from MIT's 'Neural Coding and Perception of Sound' coursehttp://ocw.mit.edu/courses/health-sciences-and-technology/hst-723j-neural-coding-and-perception-of-sound-spring-2005/index.htm
Implementing a Distributed Deep Learning Network over Sparkhttp://www.datasciencecentral.com/profiles/blogs/implementing-a-distributed-deep-learning-network-over-spark
A chess AI that learns to play chess using deep learning.https://github.com/erikbern/deep-pink
Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMindhttps://github.com/kristjankorjus/Replicating-DeepMind
Wiki2Vec. Getting Word2vec vectors for entities and word from Wikipedia Dumpshttps://github.com/idio/wiki2vec
The original code from the DeepMind article + tweakshttps://github.com/kuz/DeepMind-Atari-Deep-Q-Learner
Google deepdream - Neural Network arthttps://github.com/google/deepdream
An efficient, batched LSTM.https://gist.github.com/karpathy/587454dc0146a6ae21fc
A recurrent neural network designed to generate classical music.https://github.com/hexahedria/biaxial-rnn-music-composition
Memory Networks Implementations - Facebookhttps://github.com/facebook/MemNN
Face recognition with Google's FaceNet deep neural network.https://github.com/cmusatyalab/openface
Basic digit recognition neural networkhttps://github.com/joeledenberg/DigitRecognition
Emotion Recognition API Demo - Microsofthttps://www.projectoxford.ai/demo/emotion#detection
Proof of concept for loading Caffe models in TensorFlowhttps://github.com/ethereon/caffe-tensorflow
YOLO: Real-Time Object Detectionhttp://pjreddie.com/darknet/yolo/#webcam
AlphaGo - A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search"https://github.com/Rochester-NRT/AlphaGo
Machine Learning for Software Engineershttps://github.com/ZuzooVn/machine-learning-for-software-engineers
Machine Learning is Fun!https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.oa4rzez3g
Siraj Raval's Deep Learning tutorialshttps://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A
Dockerfacehttps://github.com/natanielruiz/dockerface
Awesome Deep Learning Musichttps://github.com/ybayle/awesome-deep-learning-music
https://patch-diff.githubusercontent.com/python2014/awesome-deep-learning#contributing
pull requesthttps://github.com/ashara12/awesome-deeplearning/pulls
https://patch-diff.githubusercontent.com/python2014/awesome-deep-learning#license
http://creativecommons.org/publicdomain/zero/1.0/
Christos Christofidishttps://linkedin.com/in/Christofidis
Readme https://patch-diff.githubusercontent.com/python2014/awesome-deep-learning#readme-ov-file
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