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README.md

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 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. [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
-21. [Deep Learning with R, Second Edition](https://www.manning.com/books/deep-learning-with-r-second-edition) - by François Chollet with Tomasz Kalinowski and J. J. Allaire
-22. [Regularization in Deep Learning](https://www.manning.com/books/regularization-in-deep-learning) - by Liu Peng
-23. [Jax in Action](https://www.manning.com/books/jax-in-action) - by Grigory Sapunov
-24. [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.knowledgeisle.com/wp-content/uploads/2019/12/2-Aur%C3%A9lien-G%C3%A9ron-Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow_-Concepts-Tools-and-Techniques-to-Build-Intelligent-Systems-O%E2%80%99Reilly-Media-2019.pdf) by Aurélien Géron  | Oct 15, 2019
+6.  [neuraltalk2](https://github.com/karpathy/neuraltalk2) by Andrej Karpathy
+7.  [An introduction to genetic algorithms](http://www.boente.eti.br/fuzzy/ebook-fuzzy-mitchell.pdf)
+8.  [Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu/)
+9.  [Deep Learning in Neural Networks: An Overview](http://arxiv.org/pdf/1404.7828v4.pdf)
+10.  [Artificial intelligence and machine learning: Topic wise explanation](https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/)
+11. [Grokking Deep Learning for Computer Vision](https://www.manning.com/books/grokking-deep-learning-for-computer-vision)
+12. [Dive into Deep Learning](https://d2l.ai/) - numpy based interactive Deep Learning book
+13. [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.
+14. [Math and Architectures of Deep Learning](https://www.manning.com/books/math-and-architectures-of-deep-learning) - by Krishnendu Chaudhury
+15. [TensorFlow 2.0 in Action](https://www.manning.com/books/tensorflow-in-action) - by Thushan Ganegedara
+16. [Deep Learning for Natural Language Processing](https://www.manning.com/books/deep-learning-for-natural-language-processing) - by Stephan Raaijmakers
+17. [Deep Learning Patterns and Practices](https://www.manning.com/books/deep-learning-patterns-and-practices) - by Andrew Ferlitsch
+18. [Inside Deep Learning](https://www.manning.com/books/inside-deep-learning) - by Edward Raff
+19. [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition) - by François Chollet
+20. [Evolutionary Deep Learning](https://www.manning.com/books/evolutionary-deep-learning) - by Micheal Lanham
+21. [Engineering Deep Learning Platforms](https://www.manning.com/books/engineering-deep-learning-platforms) - by Chi Wang and Donald Szeto
+22. [Deep Learning with R, Second Edition](https://www.manning.com/books/deep-learning-with-r-second-edition) - by François Chollet with Tomasz Kalinowski and J. J. Allaire
+23. [Regularization in Deep Learning](https://www.manning.com/books/regularization-in-deep-learning) - by Liu Peng
+24. [Jax in Action](https://www.manning.com/books/jax-in-action) - by Grigory Sapunov
+25. [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.knowledgeisle.com/wp-content/uploads/2019/12/2-Aur%C3%A9lien-G%C3%A9ron-Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow_-Concepts-Tools-and-Techniques-to-Build-Intelligent-Systems-O%E2%80%99Reilly-Media-2019.pdf) by Aurélien Géron  | Oct 15, 2019
 
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