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@@ -1,5 +1,30 @@
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# Awesome Deep Learning [](https://github.com/sindresorhus/awesome)
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+## Table of Contents
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+
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+* **[Free Online Books](#free-online-books)**
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+
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+* **[Courses](#courses)**
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+
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+* **[Videos and Lectures](#videos-and-lectures)**
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+
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+* **[Papers](#papers)**
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+
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+* **[Tutorials](#tutorials)**
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+
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+* **[Researchers](#researchers)**
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+
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+* **[WebSites](#websites)**
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+
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+* **[Datasets](#datasets)**
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+
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+* **[Frameworks](#frameworks)**
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+
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+* **[Miscellaneous](#miscellaneous)**
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+
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+* **[Contributing](#contributing)**
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+
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+
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### Free Online Books
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1. [Deep Learning](http://www.iro.umontreal.ca/~bengioy/dlbook/) by Yoshua Bengio, Ian Goodfellow and Aaron Courville (05/07/2015)
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@@ -105,6 +130,7 @@ Recognition](http://nlp.stanford.edu/~socherr/pa4_ner.pdf) [zip](http://nlp.stan
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11. [VGG Convolutional Neural Networks Practical](http://www.robots.ox.ac.uk/~vgg/practicals/cnn/index.html)
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12. [TensorFlow tutorials](https://github.com/nlintz/TensorFlow-Tutorials)
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13. [TensorFlow Python Notebooks](https://github.com/aymericdamien/TensorFlow-Examples)
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+14. [Keras and Lasagne Deep Learning Tutorials](https://github.com/Vict0rSch/deep_learning)
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## Researchers
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