Merge pull request #208 from stjepanjurekovic/master

Added Deep Learning for NLP

README.md

@@ -40,11 +40,17 @@
 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)
+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
 12. [Practical Deep Learning for Cloud, Mobile, and Edge](https://www.oreilly.com/library/view/practical-deep-learning/9781492034858/) - A book for optimization techniques during production.
 13. [Math and Architectures of Deep Learning](https://www.manning.com/books/math-and-architectures-of-deep-learning) - by Krishnendu Chaudhury
 14. [TensorFlow 2.0 in Action](https://www.manning.com/books/tensorflow-in-action) - by Thushan Ganegedara
+15. [Deep Learning for Natural Language Processing](https://www.manning.com/books/deep-learning-for-natural-language-processing) - by Stephan Raaijmakers
+16. [Deep Learning Patterns and Practices](https://www.manning.com/books/deep-learning-patterns-and-practices) - by Andrew Ferlitsch
+17. [Inside Deep Learning](https://www.manning.com/books/inside-deep-learning) - by Edward Raff
+18. [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition) - by François Chollet
+19. [Evolutionary Deep Learning](https://www.manning.com/books/evolutionary-deep-learning) - by Micheal Lanham
+20. [Engineering Deep Learning Platforms](https://www.manning.com/books/engineering-deep-learning-platforms) - by Chi Wang and Donald Szeto
  
 ### Courses
 
@@ -121,6 +127,15 @@
 24. [Deepmind x UCL Reinforcement Learning](https://www.youtube.com/playlist?list=PLqYmG7hTraZBKeNJ-JE_eyJHZ7XgBoAyb): Deep Reinforcement Learning
 25. [CMU 11-785 Intro to Deep learning Spring 2020](https://www.youtube.com/playlist?list=PLp-0K3kfddPzCnS4CqKphh-zT3aDwybDe) Course: 11-785, Intro to Deep Learning by Bhiksha Raj 
 26. [Machine Learning CS 229](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) : End part focuses on deep learning By Andrew Ng
+27. [What is Neural Structured Learning by Andrew Ferlitsch](https://youtu.be/LXWSE_9gHd0)
+28. [Deep Learning Design Patterns by Andrew Ferlitsch](https://youtu.be/_DaviS6K0Vc)
+29. [Architecture of a Modern CNN: the design pattern approach by Andrew Ferlitsch](https://youtu.be/QCGSS3kyGo0)
+30. [Metaparameters in a CNN by Andrew Ferlitsch](https://youtu.be/K1PLeggQ33I)
+31. [Multi-task CNN: a real-world example by Andrew Ferlitsch](https://youtu.be/dH2nuI-1-qM)
+32. [A friendly introduction to deep reinforcement learning by Luis Serrano](https://youtu.be/1FyAh07jh0o)
+33. [What are GANs and how do they work? by Edward Raff](https://youtu.be/f6ivp84qFUc)
+34. [Coding a basic WGAN in PyTorch by Edward Raff](https://youtu.be/7VRdaqMDalQ)
+35. [Training a Reinforcement Learning Agent by Miguel Morales](https://youtu.be/8TMT-gHlj_Q)
 
 ### Papers
 *You can also find the most cited deep learning papers from [here](https://github.com/terryum/awesome-deep-learning-papers)*