Merge branch 'master' into master

README.md

@@ -2,7 +2,7 @@
 
 ## Table of Contents
 
-* **[Free Online Books](#free-online-books)**  
+* **[Books](#books)**
 
 * **[Courses](#courses)**  
 
@@ -28,18 +28,21 @@
 
 * **[Contributing](#contributing)**  
 
- 
-### Free Online Books
+
+### Books
 
 1.  [Deep Learning](http://www.deeplearningbook.org/) by Yoshua Bengio, Ian Goodfellow and Aaron Courville  (05/07/2015)
 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) 
+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)
 5.  [neuraltalk](https://github.com/karpathy/neuraltalk) by Andrej Karpathy : numpy-based RNN/LSTM implementation
-6.  [An introduction to genetic algorithms](https://svn-d1.mpi-inf.mpg.de/AG1/MultiCoreLab/papers/ebook-fuzzy-mitchell-99.pdf)
+6.  [An introduction to genetic algorithms](http://www.boente.eti.br/fuzzy/ebook-fuzzy-mitchell.pdf)
 7.  [Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu/)
 8.  [Deep Learning in Neural Networks: An Overview](http://arxiv.org/pdf/1404.7828v4.pdf)
 9.  [Artificial intelligence and machine learning: Topic wise explanation](https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/)
+10.[Grokking Deep Learning for Computer Vision](https://www.manning.com/books/grokking-deep-learning-for-computer-vision)
+11. [Dive into Deep Learning](https://d2l.ai/) - numpy based interactive Deep Learning book
+
  
 ### Courses
 
@@ -70,6 +73,18 @@
 24. [Keras in Motion video course](https://www.manning.com/livevideo/keras-in-motion)
 25. [Practical Deep Learning For Coders](http://course.fast.ai/) by Jeremy Howard - Fast.ai
 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)
+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)
+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
 
 ### Videos and Lectures
 
@@ -80,18 +95,20 @@
 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 
+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
 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
-16.  [Deep Learning: Intelligence from Big Data](https://www.youtube.com/watch?v=czLI3oLDe8M) by Steve Jurvetson (and panel) at VLAB in Stanford. 
+16.  [Deep Learning: Intelligence from Big Data](https://www.youtube.com/watch?v=czLI3oLDe8M) by Steve Jurvetson (and panel) at VLAB in Stanford.
 17. [Introduction to Artificial Neural Networks and Deep Learning](https://www.youtube.com/watch?v=FoO8qDB8gUU) by Leo Isikdogan at Motorola Mobility HQ
 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.
 
 ### Papers
 *You can also find the most cited deep learning papers from [here](https://github.com/terryum/awesome-deep-learning-papers)*
@@ -131,8 +148,8 @@
 33. [Mastering the Game of Go with Deep Neural Networks and Tree Search](http://www.nature.com/nature/journal/v529/n7587/pdf/nature16961.pdf)
 34. [Batch Normalization](https://arxiv.org/abs/1502.03167)
 35. [Residual Learning](https://arxiv.org/pdf/1512.03385v1.pdf)
-36. [Image-to-Image Translation with Conditional Adversarial Networks](https://arxiv.org/pdf/1611.07004v1.pdf) 
-37. [Berkeley AI Research (BAIR) Laboratory](https://arxiv.org/pdf/1611.07004v1.pdf) 
+36. [Image-to-Image Translation with Conditional Adversarial Networks](https://arxiv.org/pdf/1611.07004v1.pdf)
+37. [Berkeley AI Research (BAIR) Laboratory](https://arxiv.org/pdf/1611.07004v1.pdf)
 38. [MobileNets by Google](https://arxiv.org/abs/1704.04861)
 39. [Cross Audio-Visual Recognition in the Wild Using Deep Learning](https://arxiv.org/abs/1706.05739)
 40. [Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829)
@@ -166,6 +183,9 @@
 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)
+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)
+
 
 
 
@@ -270,9 +290,10 @@
 97. [ Fei-Fei Li ](http://vision.stanford.edu/feifeili)
 98. [ Ian Goodfellow ](https://research.google.com/pubs/105214.html)
 99. [ Robert Laganière ](http://www.site.uottawa.ca/~laganier/)
+100. [Merve Ayyüce Kızrak](http://www.ayyucekizrak.com/)
 
 
-### WebSites
+### Websites
 
 1.  [deeplearning.net](http://deeplearning.net/)
 2.  [deeplearning.stanford.edu](http://deeplearning.stanford.edu/)
@@ -301,7 +322,11 @@
 25.  [Machine Learning is Fun! Adam Geitgey's Blog](https://medium.com/@ageitgey/)
 26.  [Guide to Machine Learning](http://yerevann.com/a-guide-to-deep-learning/)
 27.  [Deep Learning for Beginners](https://spandan-madan.github.io/DeepLearningProject/)
-28.  [ahmedbesbes.com)(http://ahmedbesbes.com)
+28.  [Machine Learning Mastery blog](https://machinelearningmastery.com/blog/)
+29.  [ML Compiled](https://ml-compiled.readthedocs.io/en/latest/)
+30.  [Programming Community Curated Resources](https://hackr.io/tutorials/learn-artificial-intelligence-ai)
+31.  [A Beginner's Guide To Understanding Convolutional Neural Networks](https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/)
+32.  [ahmedbesbes.com)(http://ahmedbesbes.com)
 
 ### Datasets
 
@@ -335,12 +360,12 @@
 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)
 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)
-41. [Univerity of Minnesota Vision Lab](http://vision.psych.umn.edu/www/kersten-lab/kersten-lab.html) 
+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)
 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).
@@ -354,15 +379,15 @@
 54. [IEN Image Library](http://www.ien.it/is/vislib/) - 1000+ images, mostly outdoor sequences (Formats: raw, ppm)  
 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) 
+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)
-59. [Image Analysis Laboratory](http://www.ece.ncsu.edu/imaging) 
+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](http://www-white.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) 
+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)
 69. [ftp://ftp.cps.msu.edu/pub/prip](ftp://ftp.cps.msu.edu/pub/prip) - many images (Formats: unknown)
@@ -370,39 +395,39 @@
 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)
 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 
+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 engineerign designs. (Formats: gif,vrml,wrl,stp,sat) 
-77. [Geometric & Intelligent Computing Laboratory](http://gicl.mcs.drexel.edu) 
+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)
 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)
-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)) 
-88. [LIMSI-CNRS/CHM/IMM/vision](http://www.limsi.fr/Recherche/IMM/PageIMM.html) 
+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))
+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) 
+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)
-97. [Department Image Understanding](http://www.informatik.uni-stuttgart.de/ipvr/bv/bv_home_engl.html) 
+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))
 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 )
 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.
-106. [Centre for Vision, Speech and Signal Processing](http://www.ee.surrey.ac.uk/Research/CVSSP) 
+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)
-108. [IAKS/KOGS](http://i21www.ira.uka.de) 
+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) 
+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)
 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)
@@ -417,13 +442,13 @@
 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.
 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. 
+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
 
@@ -440,6 +465,7 @@
 9. [ICDM - International Conference on Data Mining](https://www.waset.org/conference/2018/07/istanbul/ICDM)
 10. [ICCV - International Conference on Computer Vision](http://iccv2017.thecvf.com)
 11. [AAAI - Association for the Advancement of Artificial Intelligence](https://www.aaai.org)
+12. [MAIS - Montreal AI Symposium](https://montrealaisymposium.wordpress.com/)
 
 ### Frameworks
 
@@ -475,7 +501,7 @@
 29.  [Tensorflow - Open source software library for numerical computation using data flow graphs](https://github.com/tensorflow/tensorflow)
 30.  [DMTK - Microsoft Distributed Machine Learning Tookit](https://github.com/Microsoft/DMTK)
 31.  [Scikit Flow - Simplified interface for TensorFlow (mimicking Scikit Learn)](https://github.com/google/skflow)
-32.  [MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework](https://github.com/dmlc/mxnet/)
+32.  [MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework](https://github.com/apache/incubator-mxnet)
 33.  [Veles - Samsung Distributed machine learning platform](https://github.com/Samsung/veles)
 34.  [Marvin - A Minimalist GPU-only N-Dimensional ConvNets Framework](https://github.com/PrincetonVision/marvin)
 35.  [Apache SINGA - A General Distributed Deep Learning Platform](http://singa.incubator.apache.org/)
@@ -493,9 +519,16 @@
 47.  [Serpent.AI - Game agent framework: Use any video game as a deep learning sandbox](https://github.com/SerpentAI/SerpentAI)
 48.  [Caffe2 - A New Lightweight, Modular, and Scalable Deep Learning Framework](https://github.com/caffe2/caffe2)
 49.  [deeplearn.js - Hardware-accelerated deep learning and linear algebra (NumPy) library for the web](https://github.com/PAIR-code/deeplearnjs)
-50.  [TensorForce - A TensorFlow library for applied reinforcement learning](https://github.com/reinforceio/tensorforce)
+50.  [TVM - End to End Deep Learning Compiler Stack for CPUs, GPUs and specialized accelerators](https://tvm.ai/)
 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)
+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)
+58.  [Synapses - A lightweight library for neural networks that runs anywhere](https://github.com/mrdimosthenis/Synapses)
+59.  [TensorForce - A TensorFlow library for applied reinforcement learning](https://github.com/reinforceio/tensorforce)
 
 ### Tools
 
@@ -503,6 +536,10 @@
 2.  [Jupyter Notebook](http://jupyter.org) - Web-based notebook environment for interactive computing
 3.  [TensorBoard](https://github.com/tensorflow/tensorboard) - TensorFlow's Visualization Toolkit
 4.  [Visual Studio Tools for AI](https://visualstudio.microsoft.com/downloads/ai-tools-vs) - 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()`
+8.  [Neptune](https://neptune.ml/) - Lightweight tool for experiment tracking and results visualization. 
 
 ### Miscellaneous
 
@@ -532,16 +569,23 @@
 25. [Emotion Recognition API Demo - Microsoft](https://www.projectoxford.ai/demo/emotion#detection)
 26. [Proof of concept for loading Caffe models in TensorFlow](https://github.com/ethereon/caffe-tensorflow)
 27. [YOLO: Real-Time Object Detection](http://pjreddie.com/darknet/yolo/#webcam)
-28. [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)
-29. [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)
-30. [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.oa4rzez3g)
-31. [Siraj Raval's Deep Learning tutorials](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A)
-32. [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.
-33. [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
+28. [YOLO: Practical Implementation using Python](https://www.analyticsvidhya.com/blog/2018/12/practical-guide-object-detection-yolo-framewor-python/)
+29. [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)
+30. [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)
+31. [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.oa4rzez3g)
+32. [Siraj Raval's Deep Learning tutorials](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A)
+33. [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.
+34. [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
+35. [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.
+36. [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.
+37. [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.
+38. [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.
+39. [Ladder Network](https://github.com/divamgupta/ladder_network_keras) - Keras Implementation of Ladder Network for Semi-Supervised Learning 
+
 
 -----
 ### 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). 
+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