python深度学习PDF+源码

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  • 2022-03-01 15:36
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Python深度学习pdf +源代码,给有需要的人。资源来自于网络。
python 深度学习pdf+源码.zip
  • python 深度学习pdf+源码
  • deep-learning-with-python-notebooks-master
  • 8.3-neural-style-transfer.ipynb
    405.3KB
  • 6.3-advanced-usage-of-recurrent-neural-networks.ipynb
    199.4KB
  • 5.1-introduction-to-convnets.ipynb
    10.8KB
  • README.md
    3.9KB
  • 6.1-one-hot-encoding-of-words-or-characters.ipynb
    8.6KB
  • 5.3-using-a-pretrained-convnet.ipynb
    227.7KB
  • 3.7-predicting-house-prices.ipynb
    68.6KB
  • 8.5-introduction-to-gans.ipynb
    144.2KB
  • 3.6-classifying-newswires.ipynb
    62.2KB
  • 8.1-text-generation-with-lstm.ipynb
    156.8KB
  • 8.4-generating-images-with-vaes.ipynb
    277.2KB
  • 4.4-overfitting-and-underfitting.ipynb
    103.6KB
  • 6.1-using-word-embeddings.ipynb
    92.1KB
  • 2.1-a-first-look-at-a-neural-network.ipynb
    13.6KB
  • 5.4-visualizing-what-convnets-learn.ipynb
    6.7MB
  • 8.2-deep-dream.ipynb
    196.4KB
  • 3.5-classifying-movie-reviews.ipynb
    67.9KB
  • LICENSE
    1KB
  • 6.2-understanding-recurrent-neural-networks.ipynb
    82.6KB
  • 6.4-sequence-processing-with-convnets.ipynb
    92.2KB
  • 5.2-using-convnets-with-small-datasets.ipynb
    421KB
  • [muchong.com]Python深度学习.pdf
    19.1MB
内容介绍
# 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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