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Copy file name to clipboardExpand all lines: README.md
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## Table of contents
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* Chapter 2:
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* 2.1: A first look at a neural network
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*[2.1: A first look at a neural network](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/2.1-a-first-look-at-a-neural-network.ipynb)
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* Chapter 3:
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* 3.5: Classifying movie reviews
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* 3.6: Classifying newswires
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* 3.7: Predicting house prices
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*[3.5: Classifying movie reviews](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/3.5-classifying-movie-reviews.ipynb)
*[3.7: Predicting house prices](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/3.7-predicting-house-prices.ipynb)
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* Chapter 4:
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* 4.4: Underfitting and overfitting
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*[4.4: Underfitting and overfitting](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/4.4-overfitting-and-underfitting.ipynb)
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* Chapter 5:
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* 5.1: Introduction to convnets
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* 5.2: Using convnets with small datasets
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* 5.3: Using a pre-trained convnet
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* 5.4: Visualizing what convnets learn
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*[5.1: Introduction to convnets](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/5.1-introduction-to-convnets.ipynb)
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*[5.2: Using convnets with small datasets](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/5.2-using-convnets-with-small-datasets.ipynb)
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*[5.3: Using a pre-trained convnet](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/5.3-using-a-pretrained-convnet.ipynb)
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*[5.4: Visualizing what convnets learn](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/5.4-visualizing-what-convnets-learn.ipynb)
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* Chapter 6:
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* 6.1: One-hot encoding of words or characters
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* 6.1: Using word embeddings
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* 6.2: Understanding RNNs
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* 6.3: Advanced usage of RNNs
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* 6.4: Sequence processing with convnets
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*[6.1: One-hot encoding of words or characters](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/6.1-one-hot-encoding-of-words-or-characters.ipynb)
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*[6.1: Using word embeddings](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/6.1-using-word-embeddings.ipynb)
*[6.3: Advanced usage of RNNs](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/6.3-advanced-usage-of-recurrent-neural-networks.ipynb)
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*[6.4: Sequence processing with convnets](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/6.4-sequence-processing-with-convnets.ipynb)
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* Chapter 8:
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* 8.1: Text generation with LSTM
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* 8.2: Deep dream
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* 8.3: Neural style transfer
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* 8.4: Generating images with VAEs
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* 8.5: Introduction to GANs
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*[8.1: Text generation with LSTM](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/8.1-text-generation-with-lstm.ipynb)
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*[8.2: Deep dream](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/8.2-deep-dream.ipynb)
*[8.4: Generating images with VAEs](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/8.4-generating-images-with-vaes.ipynb)
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*[8.5: Introduction to GANs](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/8.5-introduction-to-gans.ipynb
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