# 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)](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). 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.
## Table of contents
* Chapter 2:
* [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)
* Chapter 3:
* [3.5: Classifying movie reviews](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/3.5-classifying-movie-reviews.ipynb)
* [3.6: Classifying newswires](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/3.6-classifying-newswires.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)
* Chapter 4:
* [4.4: Underfitting and overfitting](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/4.4-overfitting-and-underfitting.ipynb)
* Chapter 5:
* [5.1: Introduction to convnets](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/5.1-introduction-to-convnets.ipynb)
* [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)
* [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)
* [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)
* Chapter 6:
* [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)
* [6.1: Using word embeddings](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/6.1-using-word-embeddings.ipynb)
* [6.2: Understanding RNNs](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/6.2-understanding-recurrent-neural-networks.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)
* [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)
* Chapter 8:
* [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)
* [8.2: Deep dream](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/8.2-deep-dream.ipynb)
* [8.3: Neural style transfer](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/8.3-neural-style-transfer.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)
* [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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Deep Learning With Python_中文版_英文版_代码.rar (23个子文件)
Deep Learning With Python_中文版+英文版+代码
Deep_Learning_With_Python.pdf 6.67MB
Deep Learning With Python中文版.pdf 19.06MB
deep-learning-with-python-notebooks--master
deep-learning-with-python-notebooks-master
5.2-using-convnets-with-small-datasets.ipynb 421KB
8.3-neural-style-transfer.ipynb 405KB
8.2-deep-dream.ipynb 196KB
3.5-classifying-movie-reviews.ipynb 68KB
6.1-using-word-embeddings.ipynb 92KB
6.2-understanding-recurrent-neural-networks.ipynb 83KB
6.4-sequence-processing-with-convnets.ipynb 92KB
8.1-text-generation-with-lstm.ipynb 157KB
6.3-advanced-usage-of-recurrent-neural-networks.ipynb 199KB
LICENSE 1KB
8.5-introduction-to-gans.ipynb 144KB
4.4-overfitting-and-underfitting.ipynb 104KB
6.1-one-hot-encoding-of-words-or-characters.ipynb 9KB
5.3-using-a-pretrained-convnet.ipynb 228KB
8.4-generating-images-with-vaes.ipynb 277KB
5.4-visualizing-what-convnets-learn.ipynb 6.68MB
3.7-predicting-house-prices.ipynb 69KB
3.6-classifying-newswires.ipynb 62KB
README.md 4KB
5.1-introduction-to-convnets.ipynb 11KB
2.1-a-first-look-at-a-neural-network.ipynb 14KB
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