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22. Regularization in Deep Learning (https://www.manning.com/books/regularization-in-deep-learning) - by Liu Peng
23. Jax in Action (https://www.manning.com/books/jax-in-action) - by Grigory Sapunov
24. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 
(https://www.knowledgeisle.com/wp-content/uploads/2019/12/2-Aur%C3%A9lien-G%C3%A9ron-Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow_-Concepts-Tools-and-Techniques-to-Build-I
ntelligent-Systems-O%E2%80%99Reilly-Media-2019.pdf) by Aurélien Géron | Oct 15, 2019
(https://www.knowledgeisle.com/wp-content/uploads/2019/12/2-Aur%C3%A9lien-G%C3%A9ron-Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow_-Concepts-Tools-and-Techniques-to-Build-Intelligent-Systems-O%E2%80%99Reilly-Media-201
9.pdf) by Aurélien Géron | Oct 15, 2019
Courses
@@ -74,8 +74,8 @@
11. Neural Networks - usherbrooke (http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html)
12. Machine Learning - Oxford (https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) (2014-2015)
13. Deep Learning - Nvidia (https://developer.nvidia.com/deep-learning-courses) (2015)
14. Graduate Summer School: Deep Learning, Feature Learning (https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA) by Geoffrey Hinton, Yoshua Bengio, Yann LeCun, Andrew 
Ng, Nando de Freitas and several others @ IPAM, UCLA (2012)
14. Graduate Summer School: Deep Learning, Feature Learning (https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA) by Geoffrey Hinton, Yoshua Bengio, Yann LeCun, Andrew Ng, Nando de Freitas and several others @ 
IPAM, UCLA (2012)
15. Deep Learning - Udacity/Google (https://www.udacity.com/course/deep-learning--ud730) by Vincent Vanhoucke and Arpan Chakraborty (2016)
16. Deep Learning - UWaterloo (https://www.youtube.com/playlist?list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE) by Prof. Ali Ghodsi at University of Waterloo (2015)
17. Statistical Machine Learning - CMU (https://www.youtube.com/watch?v=azaLcvuql_g&list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r) by Prof. Larry Wasserman
@@ -90,16 +90,14 @@
26. Introduction to Deep Learning (http://deeplearning.cs.cmu.edu/) by Prof. Bhiksha Raj (2017)
27. AI for Everyone (https://www.deeplearning.ai/ai-for-everyone/) by Andrew Ng (2019)
28. MIT Intro to Deep Learning 7 day bootcamp (https://introtodeeplearning.com) - A seven day bootcamp designed in MIT to introduce deep learning methods and applications (2019)
29. Deep Blueberry: Deep Learning (https://mithi.github.io/deep-blueberry) - A free five-weekend plan to self-learners to learn the basics of deep-learning architectures like CNNs, LSTMs, 
RNNs, VAEs, GANs, DQN, A3C and more (2019)
29. Deep Blueberry: Deep Learning (https://mithi.github.io/deep-blueberry) - A free five-weekend plan to self-learners to learn the basics of deep-learning architectures like CNNs, LSTMs, RNNs, VAEs, GANs, DQN, A3C and more (2019)
30. Spinning Up in Deep Reinforcement Learning (https://spinningup.openai.com/) - A free deep reinforcement learning course by OpenAI (2019)
31. Deep Learning Specialization - Coursera (https://www.coursera.org/specializations/deep-learning) - Breaking into AI with the best course from Andrew NG.
32. Deep Learning - UC Berkeley | STAT-157 (https://www.youtube.com/playlist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW) by Alex Smola and Mu Li (2019)
33. Machine Learning for Mere Mortals video course (https://www.manning.com/livevideo/machine-learning-for-mere-mortals) by Nick Chase
34. Machine Learning Crash Course with TensorFlow APIs (https://developers.google.com/machine-learning/crash-course/) -Google AI
35. Deep Learning from the Foundations (https://course.fast.ai/part2) Jeremy Howard - Fast.ai
36. Deep Reinforcement Learning (nanodegree) - Udacity (https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) a 3-6 month Udacity nanodegree, spanning multiple courses
(2018)
36. Deep Reinforcement Learning (nanodegree) - Udacity (https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) a 3-6 month Udacity nanodegree, spanning multiple courses (2018)
37. Grokking Deep Learning in Motion (https://www.manning.com/livevideo/grokking-deep-learning-in-motion) by Beau Carnes (2018)
38. Face Detection with Computer Vision and Deep Learning (https://www.udemy.com/share/1000gAA0QdcV9aQng=/) by Hakan Cebeci
39. Deep Learning Online Course list at Classpert (https://classpert.com/deep-learning) List of Deep Learning online courses (some are free) from Classpert Online Course Search
@@ -123,8 +121,7 @@
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
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
15. A beginners Guide to Deep Neural Networks (http://googleresearch.blogspot.com/2015/09/a-beginners-guide-to-deep-neural.html) By Natalie Hammel and Lorraine Yurshansky
@@ -133,11 +130,10 @@
18. NIPS 2016 lecture and workshop videos (https://nips.cc/Conferences/2016/Schedule) - NIPS 2016
19. Deep Learning Crash Course (https://www.youtube.com/watch?v=oS5fz_mHVz0&list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07): a series of mini-lectures by Leo Isikdogan on YouTube (2018)
20. Deep Learning Crash Course (https://www.manning.com/livevideo/deep-learning-crash-course) By Oliver Zeigermann
21. Deep Learning with R in Motion (https://www.manning.com/livevideo/deep-learning-with-r-in-motion): a live video course that teaches how to apply deep learning to text and images using the
powerful Keras library and its R language interface.
22. Medical Imaging with Deep Learning Tutorial (https://www.youtube.com/playlist?list=PLheiZMDg_8ufxEx9cNVcOYXsT3BppJP4b): This tutorial is styled as a graduate lecture about medical imaging
with deep learning. This will cover the background of popular medical image domains (chest X-ray and histology) as well as methods to tackle multi-modality/view, segmentation, and counting 
tasks.
21. Deep Learning with R in Motion (https://www.manning.com/livevideo/deep-learning-with-r-in-motion): a live video course that teaches how to apply deep learning to text and images using the powerful Keras library and its R language 
interface.
22. Medical Imaging with Deep Learning Tutorial (https://www.youtube.com/playlist?list=PLheiZMDg_8ufxEx9cNVcOYXsT3BppJP4b): This tutorial is styled as a graduate lecture about medical imaging with deep learning. This will cover the 
background of popular medical image domains (chest X-ray and histology) as well as methods to tackle multi-modality/view, segmentation, and counting tasks.
23. Deepmind x UCL Deeplearning (https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF): 2020 version 
24. Deepmind x UCL Reinforcement Learning (https://www.youtube.com/playlist?list=PLqYmG7hTraZBKeNJ-JE_eyJHZ7XgBoAyb): Deep Reinforcement Learning
25. CMU 11-785 Intro to Deep learning Spring 2020 (https://www.youtube.com/playlist?list=PLp-0K3kfddPzCnS4CqKphh-zT3aDwybDe) Course: 11-785, Intro to Deep Learning by Bhiksha Raj 
@@ -232,13 +228,10 @@
18. Deep Learning with Python (https://www.manning.com/books/deep-learning-with-python)
19. Grokking Deep Learning (https://www.manning.com/books/grokking-deep-learning)
20. Deep Learning for Search (https://www.manning.com/books/deep-learning-for-search)
21. Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder 
(https://medium.com/sicara/keras-tutorial-content-based-image-retrieval-convolutional-denoising-autoencoder-dc91450cc511)
21. Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder (https://medium.com/sicara/keras-tutorial-content-based-image-retrieval-convolutional-denoising-autoencoder-dc91450cc511)
22. Pytorch Tutorial by Yunjey Choi (https://github.com/yunjey/pytorch-tutorial)
23. Understanding deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras 
(https://ahmedbesbes.com/understanding-deep-convolutional-neural-networks-with-a-practical-use-case-in-tensorflow-and-keras.html)
24. Overview and benchmark of traditional and deep learning models in text classification 
(https://ahmedbesbes.com/overview-and-benchmark-of-traditional-and-deep-learning-models-in-text-classification.html)
23. Understanding deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras (https://ahmedbesbes.com/understanding-deep-convolutional-neural-networks-with-a-practical-use-case-in-tensorflow-and-keras.html)
24. Overview and benchmark of traditional and deep learning models in text classification (https://ahmedbesbes.com/overview-and-benchmark-of-traditional-and-deep-learning-models-in-text-classification.html)
25. Hardware for AI: Understanding computer hardware & build your own computer (https://github.com/MelAbgrall/HardwareforAI)
26. Programming Community Curated Resources (https://hackr.io/tutorials/learn-artificial-intelligence-ai)
27. The Illustrated Self-Supervised Learning (https://amitness.com/2020/02/illustrated-self-supervised-learning/)
@@ -416,47 +409,44 @@
13. Image QA (http://www.cs.toronto.edu/~mren/imageqa/data/cocoqa/)
14. AT&T Laboratories Cambridge face database (http://www.uk.research.att.com/facedatabase.html)
15. AVHRR Pathfinder (http://xtreme.gsfc.nasa.gov)
16. Air Freight (http://www.anc.ed.ac.uk/~amos/afreightdata.html) - The Air Freight data set is a ray-traced image sequence along with ground truth segmentation based on textural 
characteristics. (455 images + GT, each 160x120 pixels). (Formats: PNG) 
17. Amsterdam Library of Object Images (http://www.science.uva.nl/~aloi/) - ALOI is a color image collection of one-thousand small objects, recorded for scientific purposes. In order to 
capture the sensory variation in object recordings, we systematically varied viewing angle, illumination angle, and illumination color for each object, and additionally captured wide-baseline
stereo images. We recorded over a hundred images of each object, yielding a total of 110,250 images for the collection. (Formats: png)
16. Air Freight (http://www.anc.ed.ac.uk/~amos/afreightdata.html) - The Air Freight data set is a ray-traced image sequence along with ground truth segmentation based on textural characteristics. (455 images + GT, each 160x120 pixels). 
(Formats: PNG) 
17. Amsterdam Library of Object Images (http://www.science.uva.nl/~aloi/) - ALOI is a color image collection of one-thousand small objects, recorded for scientific purposes. In order to capture the sensory variation in object 
recordings, we systematically varied viewing angle, illumination angle, and illumination color for each object, and additionally captured wide-baseline stereo images. We recorded over a hundred images of each object, yielding a total of
110,250 images for the collection. (Formats: png)
18. Annotated face, hand, cardiac & meat images (http://www.imm.dtu.dk/~aam/) - Most images & annotations are supplemented by various ASM/AAM analyses using the AAM-API. (Formats: bmp,asf)
19. Image Analysis and Computer Graphics (http://www.imm.dtu.dk/image/) 
21. Brown University Stimuli (http://www.cog.brown.edu/~tarr/stimuli.html) - A variety of datasets including geons, objects, and "greebles". Good for testing recognition algorithms. (Formats:
pict)
22. CAVIAR video sequences of mall and public space behavior (http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/) - 90K video frames in 90 sequences of various human activities, with XML ground 
truth of detection and behavior classification (Formats: MPEG2 & JPEG)
21. Brown University Stimuli (http://www.cog.brown.edu/~tarr/stimuli.html) - A variety of datasets including geons, objects, and "greebles". Good for testing recognition algorithms. (Formats: pict)
22. CAVIAR video sequences of mall and public space behavior (http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/) - 90K video frames in 90 sequences of various human activities, with XML ground truth of detection and behavior classification
(Formats: MPEG2 & JPEG)
23. Machine Vision Unit (http://www.ipab.inf.ed.ac.uk/mvu/)
25. CCITT Fax standard images (http://www.cs.waikato.ac.nz/~singlis/ccitt.html) - 8 images (Formats: gif)
26. CMU CIL's Stereo Data with Ground Truth (cil-ster.html) - 3 sets of 11 images, including color tiff images with spectroradiometry (Formats: gif, tiff)
27. CMU PIE Database (http://www.ri.cmu.edu/projects/project_418.html) - A database of 41,368 face images of 68 people captured under 13 poses, 43 illuminations conditions, and with 4 
different expressions.
27. CMU PIE Database (http://www.ri.cmu.edu/projects/project_418.html) - A database of 41,368 face images of 68 people captured under 13 poses, 43 illuminations conditions, and with 4 different expressions.
28. CMU VASC Image Database (http://www.ius.cs.cmu.edu/idb/) - Images, sequences, stereo pairs (thousands of images) (Formats: Sun Rasterimage)
29. Caltech Image Database (http://www.vision.caltech.edu/html-files/archive.html) - about 20 images - mostly top-down views of small objects and toys. (Formats: GIF)
30. Columbia-Utrecht Reflectance and Texture Database (http://www.cs.columbia.edu/CAVE/curet/) - Texture and reflectance measurements for over 60 samples of 3D texture, observed with over 200
different combinations of viewing and illumination directions. (Formats: bmp)
31. Computational Colour Constancy Data (http://www.cs.sfu.ca/~colour/data/index.html) - A dataset oriented towards computational color constancy, but useful for computer vision in general. 
It includes synthetic data, camera sensor data, and over 700 images. (Formats: tiff)
30. Columbia-Utrecht Reflectance and Texture Database (http://www.cs.columbia.edu/CAVE/curet/) - Texture and reflectance measurements for over 60 samples of 3D texture, observed with over 200 different combinations of viewing and 
illumination directions. (Formats: bmp)
31. Computational Colour Constancy Data (http://www.cs.sfu.ca/~colour/data/index.html) - A dataset oriented towards computational color constancy, but useful for computer vision in general. It includes synthetic data, camera sensor 
data, and over 700 images. (Formats: tiff)
32. Computational Vision Lab (http://www.cs.sfu.ca/~colour/)
34. Content-based image retrieval database (http://www.cs.washington.edu/research/imagedatabase/groundtruth/) - 11 sets of color images for testing algorithms for content-based retrieval. 
Most sets have a description file with names of objects in each image. (Formats: jpg)
34. Content-based image retrieval database (http://www.cs.washington.edu/research/imagedatabase/groundtruth/) - 11 sets of color images for testing algorithms for content-based retrieval. Most sets have a description file with names of 
objects in each image. (Formats: jpg)
35. Efficient Content-based Retrieval Group (http://www.cs.washington.edu/research/imagedatabase/)
37. Densely Sampled View Spheres (http://ls7-www.cs.uni-dortmund.de/~peters/pages/research/modeladaptsys/modeladaptsys_vba_rov.html) - Densely sampled view spheres - upper half of the view 
sphere of two toy objects with 2500 images each. (Formats: tiff)
37. Densely Sampled View Spheres (http://ls7-www.cs.uni-dortmund.de/~peters/pages/research/modeladaptsys/modeladaptsys_vba_rov.html) - Densely sampled view spheres - upper half of the view sphere of two toy objects with 2500 images 
each. (Formats: tiff)
38. Computer Science VII (Graphical Systems) (http://ls7-www.cs.uni-dortmund.de/)
40. Digital Embryos (https://web-beta.archive.org/web/20011216051535/vision.psych.umn.edu/www/kersten-lab/demos/digitalembryo.html) - Digital embryos are novel objects which may be used to 
develop and test object recognition systems. They have an organic appearance. (Formats: various formats are available on request)
40. Digital Embryos (https://web-beta.archive.org/web/20011216051535/vision.psych.umn.edu/www/kersten-lab/demos/digitalembryo.html) - Digital embryos are novel objects which may be used to develop and test object recognition systems. 
They have an organic appearance. (Formats: various formats are available on request)
41. Univerity of Minnesota Vision Lab (http://vision.psych.umn.edu/users/kersten//kersten-lab/kersten-lab.html) 
42. El Salvador Atlas of Gastrointestinal VideoEndoscopy (http://www.gastrointestinalatlas.com) - Images and Videos of his-res of studies taken from Gastrointestinal Video endoscopy. 
(Formats: jpg, mpg, gif)
42. El Salvador Atlas of Gastrointestinal VideoEndoscopy (http://www.gastrointestinalatlas.com) - Images and Videos of his-res of studies taken from Gastrointestinal Video endoscopy. (Formats: jpg, mpg, gif)
43. FG-NET Facial Aging Database (http://sting.cycollege.ac.cy/~alanitis/fgnetaging/index.htm) - Database contains 1002 face images showing subjects at different ages. (Formats: jpg)
44. FVC2000 Fingerprint Databases (http://bias.csr.unibo.it/fvc2000/) - FVC2000 is the First International Competition for Fingerprint Verification Algorithms. Four fingerprint databases 
constitute the FVC2000 benchmark (3520 fingerprints in all).
44. FVC2000 Fingerprint Databases (http://bias.csr.unibo.it/fvc2000/) - FVC2000 is the First International Competition for Fingerprint Verification Algorithms. Four fingerprint databases constitute the FVC2000 benchmark (3520 
fingerprints in all).
45. Biometric Systems Lab (http://biolab.csr.unibo.it/home.asp) - University of Bologna
46. Face and Gesture images and image sequences (http://www.fg-net.org) - Several image datasets of faces and gestures that are ground truth annotated for benchmarking
47. German Fingerspelling Database (http://www-i6.informatik.rwth-aachen.de/~dreuw/database.html) - The database contains 35 gestures and consists of 1400 image sequences that contain 
gestures of 20 different persons recorded under non-uniform daylight lighting conditions. (Formats: mpg,jpg) 
47. German Fingerspelling Database (http://www-i6.informatik.rwth-aachen.de/~dreuw/database.html) - The database contains 35 gestures and consists of 1400 image sequences that contain gestures of 20 different persons recorded under 
non-uniform daylight lighting conditions. (Formats: mpg,jpg) 
48. Language Processing and Pattern Recognition (http://www-i6.informatik.rwth-aachen.de/)
50. Groningen Natural Image Database (http://hlab.phys.rug.nl/archive.html) - 4000+ 1536x1024 (16 bit) calibrated outdoor images (Formats: homebrew)
51. ICG Testhouse sequence (http://www.icg.tu-graz.ac.at/~schindler/Data) - 2 turntable sequences from different viewing heights, 36 images each, resolution 1000x750, color (Formats: PPM)
@@ -465,115 +455,100 @@
55. INRIA's Syntim images database (http://www-rocq.inria.fr/~tarel/syntim/images.html) - 15 color image of simple objects (Formats: gif)
56. INRIA (http://www.inria.fr/)
57. INRIA's Syntim stereo databases (http://www-rocq.inria.fr/~tarel/syntim/paires.html) - 34 calibrated color stereo pairs (Formats: gif)
58. Image Analysis Laboratory (http://www.ece.ncsu.edu/imaging/Archives/ImageDataBase/index.html) - Images obtained from a variety of imaging modalities -- raw CFA images, range images and a 
host of "medical images". (Formats: homebrew)
58. Image Analysis Laboratory (http://www.ece.ncsu.edu/imaging/Archives/ImageDataBase/index.html) - Images obtained from a variety of imaging modalities -- raw CFA images, range images and a host of "medical images". (Formats: homebrew)
59. Image Analysis Laboratory (http://www.ece.ncsu.edu/imaging)
61. Image Database (http://www.prip.tuwien.ac.at/prip/image.html) - An image database including some textures 
62. JAFFE Facial Expression Image Database (http://www.mis.atr.co.jp/~mlyons/jaffe.html) - The JAFFE database consists of 213 images of Japanese female subjects posing 6 basic facial 
expressions as well as a neutral pose. Ratings on emotion adjectives are also available, free of charge, for research purposes. (Formats: TIFF Grayscale images.)
62. JAFFE Facial Expression Image Database (http://www.mis.atr.co.jp/~mlyons/jaffe.html) - The JAFFE database consists of 213 images of Japanese female subjects posing 6 basic facial expressions as well as a neutral pose. Ratings on 
emotion adjectives are also available, free of charge, for research purposes. (Formats: TIFF Grayscale images.)
63. ATR Research, Kyoto, Japan (http://www.mic.atr.co.jp/)
64. JISCT Stereo Evaluation (ftp://ftp.vislist.com/IMAGERY/JISCT/) - 44 image pairs. These data have been used in an evaluation of stereo analysis, as described in the April 1993 ARPA Image 
Understanding Workshop paper The JISCT Stereo Evaluation'' by R.C.Bolles, H.H.Baker, and M.J.Hannah, 263--274 (Formats: SSI)
64. JISCT Stereo Evaluation (ftp://ftp.vislist.com/IMAGERY/JISCT/) - 44 image pairs. These data have been used in an evaluation of stereo analysis, as described in the April 1993 ARPA Image Understanding Workshop paper The JISCT Stereo 
Evaluation'' by R.C.Bolles, H.H.Baker, and M.J.Hannah, 263--274 (Formats: SSI)
65. MIT Vision Texture (https://vismod.media.mit.edu/vismod/imagery/VisionTexture/vistex.html) - Image archive (100+ images) (Formats: ppm)
66. MIT face images and more (ftp://whitechapel.media.mit.edu/pub/images) - hundreds of images (Formats: homebrew)
67. Machine Vision (http://vision.cse.psu.edu/book/testbed/images/) - Images from the textbook by Jain, Kasturi, Schunck (20+ images) (Formats: GIF TIFF)
68. Mammography Image Databases (http://marathon.csee.usf.edu/Mammography/Database.html) - 100 or more images of mammograms with ground truth. Additional images available by request, and 
links to several other mammography databases are provided. (Formats: homebrew)
68. Mammography Image Databases (http://marathon.csee.usf.edu/Mammography/Database.html) - 100 or more images of mammograms with ground truth. Additional images available by request, and links to several other mammography databases are 
provided. (Formats: homebrew)
69. ftp://ftp.cps.msu.edu/pub/prip (ftp://ftp.cps.msu.edu/pub/prip) - many images (Formats: unknown)
70. Middlebury Stereo Data Sets with Ground Truth (http://www.middlebury.edu/stereo/data.html) - Six multi-frame stereo data sets of scenes containing planar regions. Each data set contains 9
color images and subpixel-accuracy ground-truth data. (Formats: ppm)
70. Middlebury Stereo Data Sets with Ground Truth (http://www.middlebury.edu/stereo/data.html) - Six multi-frame stereo data sets of scenes containing planar regions. Each data set contains 9 color images and subpixel-accuracy 
ground-truth data. (Formats: ppm)
71. Middlebury Stereo Vision Research Page (http://www.middlebury.edu/stereo) - Middlebury College
72. Modis Airborne simulator, Gallery and data set (http://ltpwww.gsfc.nasa.gov/MODIS/MAS/) - High Altitude Imagery from around the world for environmental modeling in support of NASA EOS 
program (Formats: JPG and HDF)
72. Modis Airborne simulator, Gallery and data set (http://ltpwww.gsfc.nasa.gov/MODIS/MAS/) - High Altitude Imagery from around the world for environmental modeling in support of NASA EOS program (Formats: JPG and HDF)
73. NIST Fingerprint and handwriting (ftp://sequoyah.ncsl.nist.gov/pub/databases/data) - datasets - thousands of images (Formats: unknown)
74. NIST Fingerprint data (ftp://ftp.cs.columbia.edu/jpeg/other/uuencoded) - compressed multipart uuencoded tar file
75. NLM HyperDoc Visible Human Project (http://www.nlm.nih.gov/research/visible/visible_human.html) - Color, CAT and MRI image samples - over 30 images (Formats: jpeg)
76. National Design Repository (http://www.designrepository.org) - Over 55,000 3D CAD and solid models of (mostly) mechanical/machined engineering designs. (Formats: gif,vrml,wrl,stp,sat) 
77. Geometric & Intelligent Computing Laboratory (http://gicl.mcs.drexel.edu)
79. OSU (MSU) 3D Object Model Database (http://eewww.eng.ohio-state.edu/~flynn/3DDB/Models/) - several sets of 3D object models collected over several years to use in object recognition 
research (Formats: homebrew, vrml)
79. OSU (MSU) 3D Object Model Database (http://eewww.eng.ohio-state.edu/~flynn/3DDB/Models/) - several sets of 3D object models collected over several years to use in object recognition research (Formats: homebrew, vrml)
80. OSU (MSU/WSU) Range Image Database (http://eewww.eng.ohio-state.edu/~flynn/3DDB/RID/) - Hundreds of real and synthetic images (Formats: gif, homebrew)
81. OSU/SAMPL Database: Range Images, 3D Models, Stills, Motion Sequences (http://sampl.eng.ohio-state.edu/~sampl/database.htm) - Over 1000 range images, 3D object models, still images and 
motion sequences (Formats: gif, ppm, vrml, homebrew)
81. OSU/SAMPL Database: Range Images, 3D Models, Stills, Motion Sequences (http://sampl.eng.ohio-state.edu/~sampl/database.htm) - Over 1000 range images, 3D object models, still images and motion sequences (Formats: gif, ppm, vrml, 
homebrew)
82. Signal Analysis and Machine Perception Laboratory (http://sampl.eng.ohio-state.edu)
84. Otago Optical Flow Evaluation Sequences (http://www.cs.otago.ac.nz/research/vision/Research/OpticalFlow/opticalflow.html) - Synthetic and real sequences with machine-readable ground truth
optical flow fields, plus tools to generate ground truth for new sequences. (Formats: ppm,tif,homebrew)
84. Otago Optical Flow Evaluation Sequences (http://www.cs.otago.ac.nz/research/vision/Research/OpticalFlow/opticalflow.html) - Synthetic and real sequences with machine-readable ground truth optical flow fields, plus tools to generate 
ground truth for new sequences. (Formats: ppm,tif,homebrew)
85. Vision Research Group (http://www.cs.otago.ac.nz/research/vision/index.html)
87. ftp://ftp.limsi.fr/pub/quenot/opflow/testdata/piv/ (ftp://ftp.limsi.fr/pub/quenot/opflow/testdata/piv/) - Real and synthetic image sequences used for testing a Particle Image Velocimetry 
application. These images may be used for the test of optical flow and image matching algorithms. (Formats: pgm (raw))
87. ftp://ftp.limsi.fr/pub/quenot/opflow/testdata/piv/ (ftp://ftp.limsi.fr/pub/quenot/opflow/testdata/piv/) - Real and synthetic image sequences used for testing a Particle Image Velocimetry application. These images may be used for the
test of optical flow and image matching algorithms. (Formats: pgm (raw))
88. LIMSI-CNRS/CHM/IMM/vision (http://www.limsi.fr/Recherche/IMM/PageIMM.html)
89. LIMSI-CNRS (http://www.limsi.fr/)
90. Photometric 3D Surface Texture Database (http://www.taurusstudio.net/research/pmtexdb/index.htm) - This is the first 3D texture database which provides both full real surface rotations 
and registered photometric stereo data (30 textures, 1680 images). (Formats: TIFF)
91. SEQUENCES FOR OPTICAL FLOW ANALYSIS (SOFA) (http://www.cee.hw.ac.uk/~mtc/sofa) - 9 synthetic sequences designed for testing motion analysis applications, including full ground truth of 
motion and camera parameters. (Formats: gif)
90. Photometric 3D Surface Texture Database (http://www.taurusstudio.net/research/pmtexdb/index.htm) - This is the first 3D texture database which provides both full real surface rotations and registered photometric stereo data (30 
textures, 1680 images). (Formats: TIFF)
91. SEQUENCES FOR OPTICAL FLOW ANALYSIS (SOFA) (http://www.cee.hw.ac.uk/~mtc/sofa) - 9 synthetic sequences designed for testing motion analysis applications, including full ground truth of motion and camera parameters. (Formats: gif)
92. Computer Vision Group (http://www.cee.hw.ac.uk/~mtc/research.html)
94. Sequences for Flow Based Reconstruction (http://www.nada.kth.se/~zucch/CAMERA/PUB/seq.html) - synthetic sequence for testing structure from motion algorithms (Formats: pgm)
95. Stereo Images with Ground Truth Disparity and Occlusion (http://www-dbv.cs.uni-bonn.de/stereo_data/) - a small set of synthetic images of a hallway with varying amounts of noise added. 
Use these images to benchmark your stereo algorithm. (Formats: raw, viff (khoros), or tiff)
96. Stuttgart Range Image Database (http://range.informatik.uni-stuttgart.de) - A collection of synthetic range images taken from high-resolution polygonal models available on the web 
(Formats: homebrew)
95. Stereo Images with Ground Truth Disparity and Occlusion (http://www-dbv.cs.uni-bonn.de/stereo_data/) - a small set of synthetic images of a hallway with varying amounts of noise added. Use these images to benchmark your stereo 
algorithm. (Formats: raw, viff (khoros), or tiff)
96. Stuttgart Range Image Database (http://range.informatik.uni-stuttgart.de) - A collection of synthetic range images taken from high-resolution polygonal models available on the web (Formats: homebrew)
97. Department Image Understanding (http://www.informatik.uni-stuttgart.de/ipvr/bv/bv_home_engl.html)
99. The AR Face Database (http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html) - Contains over 4,000 color images corresponding to 126 people's faces (70 men and 56 women). Frontal views 
with variations in facial expressions, illumination, and occlusions. (Formats: RAW (RGB 24-bit))
99. The AR Face Database (http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html) - Contains over 4,000 color images corresponding to 126 people's faces (70 men and 56 women). Frontal views with variations in facial expressions, 
illumination, and occlusions. (Formats: RAW (RGB 24-bit))
100. Purdue Robot Vision Lab (http://rvl.www.ecn.purdue.edu/RVL/)
101. The MIT-CSAIL Database of Objects and Scenes (http://web.mit.edu/torralba/www/database.html) - Database for testing multiclass object detection and scene recognition algorithms. Over 
72,000 images with 2873 annotated frames. More than 50 annotated object classes. (Formats: jpg)
102. The RVL SPEC-DB (SPECularity DataBase) (http://rvl1.ecn.purdue.edu/RVL/specularity_database/) - A collection of over 300 real images of 100 objects taken under three different 
illuminaiton conditions (Diffuse/Ambient/Directed). -- Use these images to test algorithms for detecting and compensating specular highlights in color images. (Formats: TIFF )
101. The MIT-CSAIL Database of Objects and Scenes (http://web.mit.edu/torralba/www/database.html) - Database for testing multiclass object detection and scene recognition algorithms. Over 72,000 images with 2873 annotated frames. More 
than 50 annotated object classes. (Formats: jpg)
102. The RVL SPEC-DB (SPECularity DataBase) (http://rvl1.ecn.purdue.edu/RVL/specularity_database/) - A collection of over 300 real images of 100 objects taken under three different illuminaiton conditions (Diffuse/Ambient/Directed). -- 
Use these images to test algorithms for detecting and compensating specular highlights in color images. (Formats: TIFF )
103. Robot Vision Laboratory (http://rvl1.ecn.purdue.edu/RVL/)
105. The Xm2vts database (http://xm2vtsdb.ee.surrey.ac.uk) - The XM2VTSDB contains four digital recordings of 295 people taken over a period of four months. This database contains both image 
and video data of faces.
105. The Xm2vts database (http://xm2vtsdb.ee.surrey.ac.uk) - The XM2VTSDB contains four digital recordings of 295 people taken over a period of four months. This database contains both image and video data of faces.
106. Centre for Vision, Speech and Signal Processing (http://www.ee.surrey.ac.uk/Research/CVSSP)
107. Traffic Image Sequences and 'Marbled Block' Sequence (http://i21www.ira.uka.de/image_sequences) - thousands of frames of digitized traffic image sequences as well as the 'Marbled Block' 
sequence (grayscale images) (Formats: GIF)
107. Traffic Image Sequences and 'Marbled Block' Sequence (http://i21www.ira.uka.de/image_sequences) - thousands of frames of digitized traffic image sequences as well as the 'Marbled Block' sequence (grayscale images) (Formats: GIF)
108. IAKS/KOGS (http://i21www.ira.uka.de)
110. U Bern Face images (ftp://ftp.iam.unibe.ch/pub/Images/FaceImages) - hundreds of images (Formats: Sun rasterfile)
111. U Michigan textures (ftp://freebie.engin.umich.edu/pub/misc/textures) (Formats: compressed raw)
112. U Oulu wood and knots database (http://www.ee.oulu.fi/~olli/Projects/Lumber.Grading.html) - Includes classifications - 1000+ color images (Formats: ppm)
113. UCID - an Uncompressed Colour Image Database (http://vision.doc.ntu.ac.uk/datasets/UCID/ucid.html) - a benchmark database for image retrieval with predefined ground truth. (Formats: 
tiff)
113. UCID - an Uncompressed Colour Image Database (http://vision.doc.ntu.ac.uk/datasets/UCID/ucid.html) - a benchmark database for image retrieval with predefined ground truth. (Formats: tiff)
115. UMass Vision Image Archive (http://vis-www.cs.umass.edu/~vislib/) - Large image database with aerial, space, stereo, medical images and more. (Formats: homebrew)
116. UNC's 3D image database (ftp://sunsite.unc.edu/pub/academic/computer-science/virtual-reality/3d) - many images (Formats: GIF)
117. USF Range Image Data with Segmentation Ground Truth (http://marathon.csee.usf.edu/range/seg-comp/SegComp.html) - 80 image sets (Formats: Sun rasterimage)
118. University of Oulu Physics-based Face Database (http://www.ee.oulu.fi/research/imag/color/pbfd.html) - contains color images of faces under different illuminants and camera calibration 
conditions as well as skin spectral reflectance measurements of each person.
118. University of Oulu Physics-based Face Database (http://www.ee.oulu.fi/research/imag/color/pbfd.html) - contains color images of faces under different illuminants and camera calibration conditions as well as skin spectral 
reflectance measurements of each person.
119. Machine Vision and Media Processing Unit (http://www.ee.oulu.fi/mvmp/)
121. University of Oulu Texture Database (http://www.outex.oulu.fi) - Database of 320 surface textures, each captured under three illuminants, six spatial resolutions and nine rotation 
angles. A set of test suites is also provided so that texture segmentation, classification, and retrieval algorithms can be tested in a standard manner. (Formats: bmp, ras, xv)
121. University of Oulu Texture Database (http://www.outex.oulu.fi) - Database of 320 surface textures, each captured under three illuminants, six spatial resolutions and nine rotation angles. A set of test suites is also provided so 
that texture segmentation, classification, and retrieval algorithms can be tested in a standard manner. (Formats: bmp, ras, xv)
122. Machine Vision Group (http://www.ee.oulu.fi/mvg)
124. Usenix face database (ftp://ftp.uu.net/published/usenix/faces) - Thousands of face images from many different sites (circa 994)
125. View Sphere Database (http://www-prima.inrialpes.fr/Prima/hall/view_sphere.html) - Images of 8 objects seen from many different view points. The view sphere is sampled using a geodesic 
with 172 images/sphere. Two sets for training and testing are available. (Formats: ppm)
125. View Sphere Database (http://www-prima.inrialpes.fr/Prima/hall/view_sphere.html) - Images of 8 objects seen from many different view points. The view sphere is sampled using a geodesic with 172 images/sphere. Two sets for training 
and testing are available. (Formats: ppm)
126. PRIMA, GRAVIR (http://www-prima.inrialpes.fr/Prima/)
127. Vision-list Imagery Archive (ftp://ftp.vislist.com/IMAGERY/) - Many images, many formats
128. Wiry Object Recognition Database (http://www.cs.cmu.edu/~owenc/word.htm) - Thousands of images of a cart, ladder, stool, bicycle, chairs, and cluttered scenes with ground truth labelings
of edges and regions. (Formats: jpg)
128. Wiry Object Recognition Database (http://www.cs.cmu.edu/~owenc/word.htm) - Thousands of images of a cart, ladder, stool, bicycle, chairs, and cluttered scenes with ground truth labelings of edges and regions. (Formats: jpg)
129. 3D Vision Group (http://www.cs.cmu.edu/0.000000E+003dvision/)
131. Yale Face Database (http://cvc.yale.edu/projects/yalefaces/yalefaces.html) - 165 images (15 individuals) with different lighting, expression, and occlusion configurations.
132. Yale Face Database B (http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html) - 5760 single light source images of 10 subjects each seen under 576 viewing conditions (9 poses x 64 
illumination conditions). (Formats: PGM)
132. Yale Face Database B (http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html) - 5760 single light source images of 10 subjects each seen under 576 viewing conditions (9 poses x 64 illumination conditions). (Formats: PGM)
133. Center for Computational Vision and Control (http://cvc.yale.edu/)
134. DeepMind QA Corpus (https://github.com/deepmind/rc-data) - Textual QA corpus from CNN and DailyMail. More than 300K documents in total. Paper (http://arxiv.org/abs/1506.03340) for 
reference.
135. YouTube-8M Dataset (https://research.google.com/youtube8m/) - YouTube-8M is a large-scale labeled video dataset that consists of 8 million YouTube video IDs and associated labels from a 
diverse vocabulary of 4800 visual entities.
134. DeepMind QA Corpus (https://github.com/deepmind/rc-data) - Textual QA corpus from CNN and DailyMail. More than 300K documents in total. Paper (http://arxiv.org/abs/1506.03340) for reference.
135. YouTube-8M Dataset (https://research.google.com/youtube8m/) - YouTube-8M is a large-scale labeled video dataset that consists of 8 million YouTube video IDs and associated labels from a diverse vocabulary of 4800 visual entities.
136. Open Images dataset (https://github.com/openimages/dataset) - Open Images is a dataset of ~9 million URLs to images that have been annotated with labels spanning over 6000 categories.
137. Visual Object Classes Challenge 2012 (VOC2012) (http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit) - VOC2012 dataset containing 12k images with 20 annotated classes for 
object detection and segmentation.
138. Fashion-MNIST (https://github.com/zalandoresearch/fashion-mnist) - MNIST like fashion product dataset consisting of a training set of 60,000 examples and a test set of 10,000 examples. 
Each example is a 28x28 grayscale image, associated with a label from 10 classes.
139. Large-scale Fashion (DeepFashion) Database (http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html) - Contains over 800,000 diverse fashion images. Each image in this dataset is labeled 
with 50 categories, 1,000 descriptive attributes, bounding box and clothing landmarks
137. Visual Object Classes Challenge 2012 (VOC2012) (http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit) - VOC2012 dataset containing 12k images with 20 annotated classes for object detection and segmentation.
138. Fashion-MNIST (https://github.com/zalandoresearch/fashion-mnist) - MNIST like fashion product dataset consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, 
associated with a label from 10 classes.
139. Large-scale Fashion (DeepFashion) Database (http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html) - Contains over 800,000 diverse fashion images. Each image in this dataset is labeled with 50 categories, 1,000 descriptive 
attributes, bounding box and clothing landmarks
140. FakeNewsCorpus (https://github.com/several27/FakeNewsCorpus) - Contains about 10 million news articles classified using opensources.co (http://opensources.co) types
141. LLVIP (https://github.com/bupt-ai-cz/LLVIP) - 15488 visible-infrared paired images (30976 images) for low-light vision research, Project_Page (https://bupt-ai-cz.github.io/LLVIP/)
142. MSDA (https://github.com/bupt-ai-cz/Meta-SelfLearning) - Over over 5 million images from 5 different domains for multi-source ocr/text recognition DA research, Project_Page 
(https://bupt-ai-cz.github.io/Meta-SelfLearning/)
143. SANAD: Single-Label Arabic News Articles Dataset for Automatic Text Categorization (https://data.mendeley.com/datasets/57zpx667y9/2) - SANAD Dataset is a large collection of Arabic news 
articles that can be used in different Arabic NLP tasks such as Text Classification and Word Embedding. The articles were collected using Python scripts written specifically for three popular
news websites: AlKhaleej, AlArabiya and Akhbarona. 
144. Referit3D (https://referit3d.github.io) - Two large-scale and complementary visio-linguistic datasets (aka Nr3D and Sr3D) for identifying fine-grained 3D objects in ScanNet scenes. Nr3D 
contains 41.5K natural, free-form utterances, and Sr3d contains 83.5K template-based utterances.
142. MSDA (https://github.com/bupt-ai-cz/Meta-SelfLearning) - Over over 5 million images from 5 different domains for multi-source ocr/text recognition DA research, Project_Page (https://bupt-ai-cz.github.io/Meta-SelfLearning/)
143. SANAD: Single-Label Arabic News Articles Dataset for Automatic Text Categorization (https://data.mendeley.com/datasets/57zpx667y9/2) - SANAD Dataset is a large collection of Arabic news articles that can be used in different Arabic
NLP tasks such as Text Classification and Word Embedding. The articles were collected using Python scripts written specifically for three popular news websites: AlKhaleej, AlArabiya and Akhbarona. 
144. Referit3D (https://referit3d.github.io) - Two large-scale and complementary visio-linguistic datasets (aka Nr3D and Sr3D) for identifying fine-grained 3D objects in ScanNet scenes. Nr3D contains 41.5K natural, free-form utterances,
and Sr3d contains 83.5K template-based utterances.
145. SQuAD (https://rajpurkar.github.io/SQuAD-explorer/) - Stanford released ~100,000 English QA pairs and ~50,000 unanswerable questions
146. FQuAD (https://fquad.illuin.tech/) - ~25,000 French QA pairs released by Illuin Technology
147. GermanQuAD and GermanDPR (https://www.deepset.ai/germanquad) - deepset released ~14,000 German QA pairs
@@ -651,8 +626,7 @@
51. Coach - Reinforcement Learning Coach by Intel® AI Lab (https://github.com/NervanaSystems/coach)
52. albumentations - A fast and framework agnostic image augmentation library (https://github.com/albu/albumentations)
53. Neuraxle - A general-purpose ML pipelining framework (https://github.com/Neuraxio/Neuraxle)
54. Catalyst: High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing 
(https://github.com/catalyst-team/catalyst)
54. Catalyst: High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing (https://github.com/catalyst-team/catalyst)
55. garage - A toolkit for reproducible reinforcement learning research (https://github.com/rlworkgroup/garage)
56. Detecto - Train and run object detection models with 5-10 lines of code (https://github.com/alankbi/detecto)
57. Karate Club - An unsupervised machine learning library for graph structured data (https://github.com/benedekrozemberczki/karateclub)
@@ -679,28 +653,23 @@
4. Visual Studio Tools for AI (https://www.microsoft.com/en-us/research/project/visual-studio-code-tools-ai/) - Develop, debug and deploy deep learning and AI solutions
5. TensorWatch (https://github.com/microsoft/tensorwatch) - Debugging and visualization for deep learning
6. ML Workspace (https://github.com/ml-tooling/ml-workspace) - All-in-one web-based IDE for machine learning and data science.
7. dowel (https://github.com/rlworkgroup/dowel) - A little logger for machine learning research. Log any object to the console, CSVs, TensorBoard, text log files, and more with just one call
to logger.log()
7. dowel (https://github.com/rlworkgroup/dowel) - A little logger for machine learning research. Log any object to the console, CSVs, TensorBoard, text log files, and more with just one call to logger.log()
8. Neptune (https://neptune.ai/) - Lightweight tool for experiment tracking and results visualization. 
9. CatalyzeX (https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) - Browser extension (Chrome 
(https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) and Firefox (https://addons.mozilla.org/en-US/firefox/addon/code-finder-catalyzex/)) that
automatically finds and links to code implementations for ML papers anywhere online: Google, Twitter, Arxiv, Scholar, etc.
10. Determined (https://github.com/determined-ai/determined) - Deep learning training platform with integrated support for distributed training, hyperparameter tuning, smart GPU scheduling, 
experiment tracking, and a model registry.
(https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) and Firefox (https://addons.mozilla.org/en-US/firefox/addon/code-finder-catalyzex/)) that automatically finds and links to code 
implementations for ML papers anywhere online: Google, Twitter, Arxiv, Scholar, etc.
10. Determined (https://github.com/determined-ai/determined) - Deep learning training platform with integrated support for distributed training, hyperparameter tuning, smart GPU scheduling, experiment tracking, and a model registry.
11. DAGsHub (https://dagshub.com/) - Community platform for Open Source ML – Manage experiments, data & models and create collaborative ML projects easily.
12. hub (https://github.com/activeloopai/Hub) - Fastest unstructured dataset management for TensorFlow/PyTorch by activeloop.ai. Stream & version-control data. Converts large data into single
numpy-like array on the cloud, accessible on any machine.
12. hub (https://github.com/activeloopai/Hub) - Fastest unstructured dataset management for TensorFlow/PyTorch by activeloop.ai. Stream & version-control data. Converts large data into single numpy-like array on the cloud, 
accessible on any machine.
13. DVC (https://dvc.org/) - DVC is built to make ML models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.
14. CML (https://cml.dev/) - CML helps you bring your favorite DevOps tools to machine learning.
15. MLEM (https://mlem.ai/) - MLEM is a tool to easily package, deploy and serve Machine Learning models. It seamlessly supports a variety of scenarios like real-time serving and batch 
processing.
15. MLEM (https://mlem.ai/) - MLEM is a tool to easily package, deploy and serve Machine Learning models. It seamlessly supports a variety of scenarios like real-time serving and batch processing.
Miscellaneous
1. Caffe Webinar 
(http://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.php?searchByKeyword=shelhamer&searchItems=&sessionTopic=&sessionEvent=4&sessionYear=2014&sessionFormat=&subm
it=&select=+)
1. Caffe Webinar (http://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.php?searchByKeyword=shelhamer&searchItems=&sessionTopic=&sessionEvent=4&sessionYear=2014&sessionFormat=&submit=&select=+)
2. 100 Best Github Resources in Github for DL (http://meta-guide.com/software-meta-guide/100-best-github-deep-learning/)
3. Word2Vec (https://code.google.com/p/word2vec/)
4. Caffe DockerFile (https://github.com/tleyden/docker/tree/master/caffe)
@@ -709,10 +678,8 @@
7. Torch7 Cheat sheet (https://github.com/torch/torch7/wiki/Cheatsheet)
8. 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/)
9. 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/)
10. 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/)
11. 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)
10. 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/)
11. 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)
12. Implementing a Distributed Deep Learning Network over Spark (http://www.datasciencecentral.com/profiles/blogs/implementing-a-distributed-deep-learning-network-over-spark)
13. A chess AI that learns to play chess using deep learning. (https://github.com/erikbern/deep-pink)
14. Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind (https://github.com/kristjankorjus/Replicating-DeepMind)
@@ -734,12 +701,10 @@
30. Siraj Raval's Deep Learning tutorials (https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A)
31. Dockerface (https://github.com/natanielruiz/dockerface) - Easy to install and use deep learning Faster R-CNN face detection for images and video in a docker container.
32. Awesome Deep Learning Music (https://github.com/ybayle/awesome-deep-learning-music) - Curated list of articles related to deep learning scientific research applied to music
33. Awesome Graph Embedding (https://github.com/benedekrozemberczki/awesome-graph-embedding) - Curated list of articles related to deep learning scientific research on graph structured data 
at the graph level.
34. Awesome Network Embedding (https://github.com/chihming/awesome-network-embedding) - Curated list of articles related to deep learning scientific research on graph structured data at the 
node level.
35. Microsoft Recommenders (https://github.com/Microsoft/Recommenders) contains examples, utilities and best practices for building recommendation systems. Implementations of several 
state-of-the-art algorithms are provided for self-study and customization in your own applications.
33. Awesome Graph Embedding (https://github.com/benedekrozemberczki/awesome-graph-embedding) - Curated list of articles related to deep learning scientific research on graph structured data at the graph level.
34. Awesome Network Embedding (https://github.com/chihming/awesome-network-embedding) - Curated list of articles related to deep learning scientific research on graph structured data at the node level.
35. Microsoft Recommenders (https://github.com/Microsoft/Recommenders) contains examples, utilities and best practices for building recommendation systems. Implementations of several state-of-the-art algorithms are provided for 
self-study and customization in your own applications.
36. The Unreasonable Effectiveness of Recurrent Neural Networks (http://karpathy.github.io/2015/05/21/rnn-effectiveness/) - Andrej Karpathy blog post about using RNN for generating text.
37. Ladder Network (https://github.com/divamgupta/ladder_network_keras) - Keras Implementation of Ladder Network for Semi-Supervised Learning 
38. toolbox: Curated list of ML libraries (https://github.com/amitness/toolbox)
@@ -747,11 +712,11 @@
40. AI Expert Roadmap (https://github.com/AMAI-GmbH/AI-Expert-Roadmap) - Roadmap to becoming an Artificial Intelligence Expert
41. Awesome Drug Interactions, Synergy, and Polypharmacy Prediction (https://github.com/AstraZeneca/awesome-polipharmacy-side-effect-prediction/)
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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 (https://github.com/ashara12/awesome-deeplearning/pulls).
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License
!CC0 (http://i.creativecommons.org/p/zero/1.0/88x31.png) (http://creativecommons.org/publicdomain/zero/1.0/)