ShusenTang / Deep Learning With Pytorch Chinese
本仓库将PyTorch官方书籍《Deep learning with PyTorch》（基本摘录版）翻译成中文版并给出可运行的相关代码。
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Deep-Learning-with-PyTorch-Chinese
本项目（网页版传送门）将PyTorch官方书籍《Deep learning with PyTorch》（基本摘录版）翻译成中文并给出全书可运行的相关代码。
This project translates the PyTorch official book "Deep learning with PyTorch" (essential excerpt version) into Chinese.
1. 书籍简介
自 2016 年诞生以来，PyTorch 已经成为当今最火热的深度学习框架之一。最近，官方权威的 PyTorch 教程书《Deep learning with PyTorch》终于问世了，消息一出就获得巨佬 Yann LeCun 力荐，是入门PyTorch及深度学习的绝佳教材。
需要注意的是，PyTorch官网提供的PDF是基本摘录版（Essential Excerpts），共141页，内容包括以下五个部分：
- 深度学习与PyTorch简介
- 从一个张量开始
- 使用张量表示真实数据
- 学习机制
- 使用神经网络拟合数据
因此可作为快速入门PyTorch的教程。此书完整版目前也可免费预览，传送门。
2. 项目简介
本项目将原书翻译成中文并且给出可运行的相关代码。
本仓库主要包含code和docs两个文件夹（外加一些数据存放在data中）。其中code文件夹就是每章相关jupyter notebook代码；docs文件夹就是markdown格式的《Deep learning with PyTorch》（基本摘录版）书中的相关内容的中文翻译，然后利用docsify将网页文档部署到GitHub Pages上。欢迎对本项目做出贡献或提出issue。
3. 使用方法
本项目面向对PyTorch感兴趣，尤其是想快速入门PyTorch的童鞋。本项目并不要求你有任何深度学习或者机器学习的背景知识，你只需了解基础的数学和编程，如基础的线性代数、微分和概率，以及基础的Python编程。
本仓库的文档包含一些latex公式，但github的markdown原生是不支持公式显示的，而docs文件夹已经利用docsify被部署到了GitHub Pages上，所以你可以方便地访问本项目网页版。如果你想跑一下相关代码的话需要把本项目clone下来，然后运行code文件夹下相关代码。
由于本项目所翻译的是基本摘录版，仅141页，所以适合快速入门PyTorch。如果你想对PyTorch以及深度学习（例如计算机视觉、自然语言处理等）有更深入的学习，可能还需要更多的资料，感兴趣的可以参考我的另一个项目Dive-into-DL-PyTorch。
4. 目录
5. 声明
- 译者纯粹出于学习目的与个人兴趣而进行翻译，不追求任何经济利益；
- 本项目仅限于学习研究目的的使用，译者保留对此项目的署名权，任何转载必须注明出处，但不得用于任何商业用途；
- 使用本项目对原著的侵权行为或者违反知识产权保护法的任何行为，与译者无关；
- 有能力阅读英文书籍者请阅读原版或购买完整版书籍。
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CC BY-NC（署名-非商业性使用）4.0
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