Python深度学习.pdf+代码
所属分类：人工智能/神经网络/深度学习
开发工具：Python
文件大小：24531KB
下载次数：1586
上传日期：2018-10-10 13:43:44
上 传 者：
wangjindian00
说明： 本书由Keras之父、现任Google人工智能研究员的弗朗索瓦?肖莱（Franc?ois Chollet）执笔，详尽介绍了用Python和Keras进行深度学习的探索实践，包括计算机视觉、自然语言处理、产生式模型等应用。书中包含30多个代码示例，步骤讲解详细透彻。由于本书立足于人工智能的可达性和大众化，读者无须具备机器学习相关背景知识即可展开阅读。在学习完本书后，读者将具备搭建自己的深度学习环境、建立图像识别模型、生成图像和文字等能力。
(This book is written by Franc_ois Chollet, the father of Keras and a current researcher on artificial intelligence at Google. It introduces in detail the exploratory practice of using Python and Keras for in-depth learning, including computer vision, natural language processing, production models and other applications. The book contains more than 30 code examples, the steps to explain in detail. Because the book is based on the accessibility and popularity of artificial intelligence, readers need not have the background knowledge of machine learning to start reading. After learning this book, readers will have the ability to build their own in-depth learning environment, establish image recognition model, generate images and text.)
文件列表:
Python深度学习.pdf (19984989, 2018-09-13)
Python深度学习代码 (0, 2017-11-19)
Python深度学习代码\2.1-a-first-look-at-a-neural-network.ipynb (13940, 2017-11-19)
Python深度学习代码\3.5-classifying-movie-reviews.ipynb (69517, 2017-11-19)
Python深度学习代码\3.6-classifying-newswires.ipynb (63700, 2017-11-19)
Python深度学习代码\3.7-predicting-house-prices.ipynb (70240, 2017-11-19)
Python深度学习代码\4.4-overfitting-and-underfitting.ipynb (106102, 2017-11-19)
Python深度学习代码\5.1-introduction-to-convnets.ipynb (11100, 2017-11-19)
Python深度学习代码\5.2-using-convnets-with-small-datasets.ipynb (431140, 2017-11-19)
Python深度学习代码\5.3-using-a-pretrained-convnet.ipynb (233147, 2017-11-19)
Python深度学习代码\5.4-visualizing-what-convnets-learn.ipynb (7004823, 2017-11-19)
Python深度学习代码\6.1-one-hot-encoding-of-words-or-characters.ipynb (8792, 2017-11-19)
Python深度学习代码\6.1-using-word-embeddings.ipynb (94317, 2017-11-19)
Python深度学习代码\6.2-understanding-recurrent-neural-networks.ipynb (84619, 2017-11-19)
Python深度学习代码\6.3-advanced-usage-of-recurrent-neural-networks.ipynb (204233, 2017-11-19)
Python深度学习代码\6.4-sequence-processing-with-convnets.ipynb (94418, 2017-11-19)
Python深度学习代码\8.1-text-generation-with-lstm.ipynb (160597, 2017-11-19)
Python深度学习代码\8.2-deep-dream.ipynb (201125, 2017-11-19)
Python深度学习代码\8.3-neural-style-transfer.ipynb (415051, 2017-11-19)
Python深度学习代码\8.4-generating-images-with-vaes.ipynb (283828, 2017-11-19)
Python深度学习代码\8.5-introduction-to-gans.ipynb (147649, 2017-11-19)
Python深度学习代码\LICENSE (1074, 2017-11-19)
# 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=765***dff). 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)
* [***: Sequence processing with convnets](http://nbviewer.jupyter.org/github/fchollet/deep-learning-with-python-notebooks/blob/master/***-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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