Merge branch 'master' into master

README.md

@@ -37,6 +37,7 @@
 6.  [An introduction to genetic algorithms](https://svn-d1.mpi-inf.mpg.de/AG1/MultiCoreLab/papers/ebook-fuzzy-mitchell-99.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/)
 
 ### Courses
 
@@ -88,6 +89,7 @@
 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)
 
 ### Papers
 *You can also find the most cited deep learning papers from [here](https://github.com/terryum/awesome-deep-learning-papers)*
@@ -133,6 +135,7 @@
 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
 
@@ -487,6 +490,7 @@
 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)
 
 ### Miscellaneous
 
@@ -497,7 +501,7 @@
 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 +526,7 @@
 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. [Netron](https://github.com/lutzroeder/netron) - Visualizer for deep learning and machine learning models
 
 -----
 ### Contributing