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| keras | Keras is an open source neural network library written in Python. It is capable of running on top of either Tensorflow or Theano. |
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|[setup](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/0.%20Preamble.ipynb)| Learn about the tutorial goals and how to set up your Keras environment. |
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|[intro-deep-learning-ann](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/1.1%20Introduction%20-%20Deep%20Learning%20and%20ANN.ipynb)| Get an intro to deep learning with Keras and Artificial Neural Networks (ANN). |
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|[theano](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/1.2%20Introduction%20-%20Theano.ipynb)| Learn about Theano by working with weights matrices and gradients. |
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|[keras-otto](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/1.3%20Introduction%20-%20Keras.ipynb)| Learn about Keras by looking at the Kaggle Otto challenge. |
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|[ann-mnist](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/1.4%20(Extra)%20A%20Simple%20Implementation%20of%20ANN%20for%20MNIST.ipynb)| Review a simple implementation of ANN for MNIST using Keras. |
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|[conv-nets](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/2.1%20Supervised%20Learning%20-%20ConvNets.ipynb)| Learn about Convolutional Neural Networks (CNNs) with Keras. |
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|[conv-net-1](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/2.2.1%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20I.ipynb)| Recognize handwritten digits from MNIST using Keras - Part 1. |
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|[conv-net-2](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/2.2.2%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20II.ipynb)| Recognize handwritten digits from MNIST using Keras - Part 2. |
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|[keras-models](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/2.3%20Supervised%20Learning%20-%20Famous%20Models%20with%20Keras.ipynb)| Use pre-trained models such as VGG16, VGG19, ResNet50, and Inception v3 with Keras. |
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|[auto-encoders](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/3.1%20Unsupervised%20Learning%20-%20AutoEncoders%20and%20Embeddings.ipynb)| Learn about Autoencoders with Keras. |
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|[rnn-lstm](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/3.2%20RNN%20and%20LSTM.ipynb)| Learn about Recurrent Neural Networks (RNNs) with Keras. |
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|[lstm-sentence-gen](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/3.3%20(Extra)%20LSTM%20for%20Sentence%20Generation.ipynb)| Learn about RNNs using Long Short Term Memory (LSTM) networks with Keras. |
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### deep-learning-misc
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| Notebook | Description |
@@ -331,6 +354,7 @@ Notebooks tested with Python 2.7.x.
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*[TensorFlow Tutorials](https://github.com/alrojo/tensorflow-tutorial) by Alexander R Johansen
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*[TensorFlow Book](https://github.com/BinRoot/TensorFlow-Book) by Nishant Shukla
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*[Summer School 2015](https://github.com/mila-udem/summerschool2015) by mila-udem
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*[Keras tutorials](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio
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