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2.1-a-first-look-at-a-neural-network.ipynb
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3.5-classifying-movie-reviews.ipynb
3.5-classifying-movie-reviews.ipynb
3.6-classifying-newswires.ipynb
3.6-classifying-newswires.ipynb
3.7-predicting-house-prices.ipynb
3.7-predicting-house-prices.ipynb
4.4-overfitting-and-underfitting.ipynb
4.4-overfitting-and-underfitting.ipynb
5.1-introduction-to-convnets.ipynb
5.1-introduction-to-convnets.ipynb
5.2-using-convnets-with-small-datasets.ipynb
5.2-using-convnets-with-small-datasets.ipynb
5.3-using-a-pretrained-convnet.ipynb
5.3-using-a-pretrained-convnet.ipynb
5.4-visualizing-what-convnets-learn.ipynb
5.4-visualizing-what-convnets-learn.ipynb
6.1-one-hot-encoding-of-words-or-characters.ipynb
6.1-one-hot-encoding-of-words-or-characters.ipynb
6.1-using-word-embeddings.ipynb
6.1-using-word-embeddings.ipynb
6.2-understanding-recurrent-neural-networks.ipynb
6.2-understanding-recurrent-neural-networks.ipynb
6.3-advanced-usage-of-recurrent-neural-networks.ipynb
6.3-advanced-usage-of-recurrent-neural-networks.ipynb
6.4-sequence-processing-with-convnets.ipynb
6.4-sequence-processing-with-convnets.ipynb
8.1-text-generation-with-lstm.ipynb
8.1-text-generation-with-lstm.ipynb
8.2-deep-dream.ipynb
8.2-deep-dream.ipynb
8.3-neural-style-transfer.ipynb
8.3-neural-style-transfer.ipynb
8.4-generating-images-with-vaes.ipynb
8.4-generating-images-with-vaes.ipynb
8.5-introduction-to-gans.ipynb
8.5-introduction-to-gans.ipynb
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Companion Jupyter notebooks for the book "Deep Learning with Python"

This repository contains Jupyter notebooks implementing the code samples found in the book Deep Learning with Python (Manning Publications). Note that the original text of the book features far more content than you will find in these notebooks, in particular further explanations and figures. Here we have only included the code samples themselves and immediately related surrounding comments.

These notebooks use Python 3.6 and Keras 2.0.8. They were generated on a p2.xlarge EC2 instance.

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Jupyter notebooks for the code samples of the book "Deep Learning with Python"

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