Merge branch 'master' into master

README.md

@@ -14,7 +14,7 @@
 
 * **[Researchers](#researchers)**  
 
-* **[WebSites](#websites)**  
+* **[Websites](#websites)**  
 
 * **[Datasets](#datasets)**
 
@@ -22,6 +22,8 @@
 
 * **[Frameworks](#frameworks)**  
 
+* **[Tools](#tools)**  
+
 * **[Miscellaneous](#miscellaneous)**  
 
 * **[Contributing](#contributing)**  
@@ -34,10 +36,12 @@
 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. [Dive into Deep Learning](https://d2l.ai/) - numpy based interactive Deep Learning book
+ 
 ### Courses
 
 1.  [Machine Learning - Stanford](https://class.coursera.org/ml-005) by Andrew Ng in Coursera (2010-2014)
@@ -68,6 +72,12 @@
 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. [Machine Learning Crash Course with TensorFlow APIs](https://developers.google.com/machine-learning/crash-course/) -Google AI
+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)
 
 ### Videos and Lectures
 
@@ -135,6 +145,7 @@
 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)
 41. [Matrix Capsules With Em Routing](https://openreview.net/pdf?id=HJWLfGWRb)
+42. [Efficient BackProp](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
 
 ### Tutorials
 
@@ -335,7 +346,7 @@
 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).
@@ -367,7 +378,7 @@
 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 engineerign designs. (Formats: gif,vrml,wrl,stp,sat)
+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)
@@ -420,6 +431,7 @@
 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
 
 ### Conferences
 
@@ -469,7 +481,7 @@
 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/)
@@ -487,6 +499,19 @@
 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)
+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.  [garage - A toolkit for reproducible reinforcement learning research](https://github.com/rlworkgroup/garage)
+
+### Tools
+
+1.  [Netron](https://github.com/lutzroeder/netron) - Visualizer for deep learning and machine learning models
+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.  [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()`
+6.  [Neptune](https://neptune.ml/) - Lightweight tool for experiment tracking and results visualization. 
 
 ### Miscellaneous
 
@@ -497,7 +522,7 @@
 5.  [Caffe DockerFile](https://github.com/tleyden/docker/tree/master/caffe)
 6.  [TorontoDeepLEarning convnet](https://github.com/TorontoDeepLearning/convnet)
 8.  [gfx.js](https://github.com/clementfarabet/gfx.js)
-9. [Torch7 Cheat sheet](https://github.com/torch/torch7/wiki/Cheatsheet)
+9.  [Torch7 Cheat sheet](https://github.com/torch/torch7/wiki/Cheatsheet)
 10. [Misc from MIT's 'Advanced Natural Language Processing' course](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-864-advanced-natural-language-processing-fall-2005/)
 11. [Misc from MIT's 'Machine Learning' course](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/)
 12. [Misc from MIT's 'Networks for Learning: Regression and Classification' course](http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-520-a-networks-for-learning-regression-and-classification-spring-2001/)
@@ -522,6 +547,8 @@
 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.
+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.
 
 -----
 ### Contributing