Merge pull request #101 from dheeraj-coding/master

Added Google's brand new course

README.md

@@ -28,12 +28,12 @@
 
 * **[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)
@@ -71,6 +71,7 @@
 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. [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)
@@ -87,15 +88,15 @@
 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)
@@ -138,8 +139,8 @@
 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)
@@ -340,7 +341,7 @@
 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/)
@@ -359,15 +360,15 @@
 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)
@@ -375,39 +376,39 @@
 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 engineering designs. (Formats: gif,vrml,wrl,stp,sat) 
-77. [Geometric & Intelligent Computing Laboratory](http://gicl.mcs.drexel.edu) 
+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)
@@ -422,13 +423,13 @@
 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
 
@@ -551,7 +552,7 @@
 
 -----
 ### 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