Merge branch 'master' into patch-1

README.md

@@ -28,18 +28,19 @@
 
 * **[Contributing](#contributing)**  
 
- 
+
 ### Free Online 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](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. [Dive into Deep Learning](https://d2l.ai/) - numpy based interactive Deep Learning book
  
 ### Courses
 
@@ -74,6 +75,12 @@
 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)
 
 ### Videos and Lectures
 
@@ -84,15 +91,15 @@
 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)
@@ -135,8 +142,8 @@
 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)
@@ -168,6 +175,7 @@
 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://blog.sicara.com/keras-tutorial-content-based-image-retrieval-convolutional-denoising-autoencoder-dc91450cc511)
 22. [Pytorch Tutorial by Yunjey Choi](https://github.com/yunjey/pytorch-tutorial)
+23. [Programming Community Curated Resources](https://hackr.io/tutorials/learn-artificial-intelligence-ai)
 
 
 
@@ -304,6 +312,7 @@
 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.  [Programming Community Curated Resources](https://hackr.io/tutorials/learn-artificial-intelligence-ai)
 
 ### Datasets
 
@@ -337,7 +346,7 @@
 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/)
@@ -356,15 +365,15 @@
 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)
@@ -372,39 +381,39 @@
 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)
@@ -419,13 +428,13 @@
 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
 
@@ -477,7 +486,7 @@
 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/)
@@ -499,6 +508,9 @@
 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.  [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.  [garage - A toolkit for reproducible reinforcement learning research](https://github.com/rlworkgroup/garage)
+55.  [Detecto - Train and run object detection models with 5-10 lines of code](https://github.com/alankbi/detecto)
+56.  [Karate Club - An unsupervised machine learning library for graph structured data](https://github.com/benedekrozemberczki/karateclub)
 
 ### Tools
 
@@ -506,6 +518,9 @@
 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. [ML Workspace](https://github.com/ml-tooling/ml-workspace) - All-in-one web-based IDE for machine learning and data science.
+6.  [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.  [Neptune](https://neptune.ml/) - Lightweight tool for experiment tracking and results visualization. 
 
 ### Miscellaneous
 
@@ -541,12 +556,12 @@
 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
-34. [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 Graph Embedding](https://github.com/benedekrozemberczki/awesome-graph-classification) - Curated list of articles related to deep learning scientific research on graph structured data at the graph level.
 35. [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.
 
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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). 
+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