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*.md linguist-language=python

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Content.md

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### 2.[PyTorch环境搭建](https://github.com/fendouai/PyTorchDocs/blob/master/FirstSection/InstallIutorial.md)
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## 第二章:PyTorch之60min入门
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### 1.张量:[Tensor](https://github.com/fendouai/PyTorchDocs/blob/master/SecondSection/Tensor.md)
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### 2.自动求导:Autograd
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### 3.神经网络
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### 4.图像分类器
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### 5.数据并行化处理
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### 1.[PyTorch 入门](https://github.com/fendouai/PyTorchDocs/blob/master/SecondSection/what_is_pytorch.md)
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### 2.[PyTorch 自动微分](https://github.com/fendouai/PyTorchDocs/blob/master/SecondSection/autograd_automatic_differentiation.md)
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### 3.[PyTorch 神经网络](https://github.com/fendouai/PyTorchDocs/blob/master/SecondSection/neural_networks.md)
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### 4.[PyTorch 图像分类器](https://github.com/fendouai/PyTorchDocs/blob/master/SecondSection/training_a_classifier.md)
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### 5.[PyTorch 数据并行处理](https://github.com/fendouai/PyTorchDocs/blob/master/SecondSection/optional_data_parallelism.md)
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## 第三章:PyTorch之入门强化
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### 1.[数据加载和处理](https://github.com/fendouai/PyTorchDocs/blob/master/ThirdSection/DataLoding.md)
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### 4.[在深度学习和NLP中使用Pytorch](https://github.com/fendouai/PyTorchDocs/blob/master/FifthSection/DeepLearning%20NLP.md)
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### 5.[使用Sequence2Sequence网络和注意力进行翻译](https://github.com/fendouai/PyTorchDocs/blob/master/FifthSection/Translation_S2S%20Network.md)
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## 第六章:PyTorch之生成对抗网络
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## 第七章:PyTorch之强化学习
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## 第八章:在生产中部署PyTorch模型
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### 1.通过带Flask的REST API在Python中部署PyTorch
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### 1.通过带Flask的REST API在Python中部署PyTorch
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=======
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## 第六章:[PyTorch之深度卷积对抗生成网络](https://github.com/fendouai/PyTorchDocs/blob/master/SixthSection/Dcgan.md)
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## 第七章:[PyTorch之强化学习(DQN)教程](https://github.com/fendouai/PyTorchDocs/blob/master/SeventhSection/ReinforcementLearning.md)
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磐创AI 聊天机器人,智能客服:
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[http://www.panchuangai.com/](http://www.panchuangai.com/)
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磐创教程网站,TensorFlow,Pytorch,Keras:
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[http://panchuang.net/](http://panchuang.net/)
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魔图互联 知识图谱推荐系统:
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[http://motuhulian.com](http://motuhulian.com)
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README.md

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# PyTorchDocs
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# 简介
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hello world
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目前研究人员正在使用的深度学习框架不尽相同,有 TensorFlow 、PyTorch、Keras等。这些深度学习框架被应用于计算机视觉、语音识别、自然语言处理与生物信息学等领域,并获取了极好的效果。其中,PyTorch是当前难得的简洁优雅且高效快速的框架,当前开源的框架中,没有哪一个框架能够在灵活性、易用性、速度这三个方面有两个能同时超过PyTorch。
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# 先定 readme 内容的框架
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# 然后填充内容
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本文档的定位是 PyTorch 入门教程,主要针对想要学习PyTorch的学生群体或者深度学习爱好者。通过教程的学习,能够实现零基础想要了解和学习深度学习,降低自学的难度,快速学习PyTorch。
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官方教程包含了 PyTorch 介绍,安装教程;60分钟快速入门教程,可以迅速从小白阶段完成一个分类器模型;计算机视觉常用模型,方便基于自己的数据进行调整,不再需要从头开始写;自然语言处理模型,聊天机器人,文本生成等生动有趣的项目。
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总而言之:
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* 如果你想了解一下 PyTorch,可以看介绍部分。
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* 如果你想快速入门 PyTorch,可以看60分钟快速入门。
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* 如果你想解决计算机视觉问题,可以看计算机视觉部分。
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* 如果你想解决自然语言处理问题,可以看NLP 部分。
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* 还有强化学习和生成对抗网络部分内容。
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作者:[磐创AI](http://www.panchuangai.com/) [PyTorch](http://pytorch123.com/) 翻译小组: News & PanChuang
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原文:[https://pytorch.org/tutorials/](https://pytorch.org/tutorials/)
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# 目录
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## 第一章:PyTorch之简介与下载
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### 1.[PyTorch简介](https://github.com/fendouai/PyTorchDocs/blob/master/FirstSection/PyTorchIntro.md)
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### 2.[PyTorch环境搭建](https://github.com/fendouai/PyTorchDocs/blob/master/FirstSection/InstallIutorial.md)
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## 第二章:PyTorch之60min入门
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### 1.[PyTorch 入门](https://github.com/fendouai/PyTorchDocs/blob/master/SecondSection/what_is_pytorch.md)
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### 2.[PyTorch 自动微分](https://github.com/fendouai/PyTorchDocs/blob/master/SecondSection/autograd_automatic_differentiation.md)
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### 3.[PyTorch 神经网络](https://github.com/fendouai/PyTorchDocs/blob/master/SecondSection/neural_networks.md)
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### 4.[PyTorch 图像分类器](https://github.com/fendouai/PyTorchDocs/blob/master/SecondSection/training_a_classifier.md)
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### 5.[PyTorch 数据并行处理](https://github.com/fendouai/PyTorchDocs/blob/master/SecondSection/optional_data_parallelism.md)
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## 第三章:PyTorch之入门强化
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### 1.[数据加载和处理](https://github.com/fendouai/PyTorchDocs/blob/master/ThirdSection/DataLoding.md)
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### 2.[PyTorch小试牛刀](https://github.com/fendouai/PyTorchDocs/blob/master/ThirdSection/LearningPyTorch.md)
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### 3.[迁移学习](https://github.com/fendouai/PyTorchDocs/blob/master/ThirdSection/TransferLearning.md)
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### 4.[混合前端的seq2seq模型部署](https://github.com/fendouai/PyTorchDocs/blob/master/ThirdSection/DeployingSeq2SeqModelwithHybridFrontend.MD)
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### 5.[保存和加载模型](https://github.com/fendouai/PyTorchDocs/blob/master/ThirdSection/SaveModel.md)
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## 第四章:PyTorch之图像篇
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### 1.[微调基于torchvision 0.3的目标检测模型](https://github.com/fendouai/PyTorchDocs/blob/master/fourSection/ObjectDetectionFinetuning.md)
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### 2.[微调TorchVision模型](https://github.com/fendouai/PyTorchDocs/blob/master/fourSection/FinetuningTorchVisionModel.md)
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### 3.[空间变换器网络](https://github.com/fendouai/PyTorchDocs/blob/master/fourSection/SpatialTranNet.md)
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### 4.[使用PyTorch进行Neural-Transfer](https://github.com/fendouai/PyTorchDocs/blob/master/fourSection/NeuralTransfer.md)
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### 5.[生成对抗示例](https://github.com/fendouai/PyTorchDocs/blob/master/fourSection/AdversarialExampleGene.md)
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### 6.[使用ONNX将模型转移至Caffe2和移动端](https://github.com/fendouai/PyTorchDocs/blob/master/fourSection/ONNX.md)
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## 第五章:PyTorch之文本篇
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### 1.[聊天机器人教程](https://github.com/fendouai/PyTorchDocs/blob/master/FifthSection/Chatbot.md)
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### 2.[使用字符级RNN生成名字](https://github.com/fendouai/PyTorchDocs/blob/master/FifthSection/Char%20RNN%20Generation.MD)
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### 3.[使用字符级RNN进行名字分类](https://github.com/fendouai/PyTorchDocs/blob/master/FifthSection/Char%20RNN%20Classification.md)
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### 4.[在深度学习和NLP中使用Pytorch](https://github.com/fendouai/PyTorchDocs/blob/master/FifthSection/DeepLearning%20NLP.md)
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### 5.[使用Sequence2Sequence网络和注意力进行翻译](https://github.com/fendouai/PyTorchDocs/blob/master/FifthSection/Translation_S2S%20Network.md)
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## 第六章:PyTorch之生成对抗网络
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### 1.[生成对抗网络(Generative Adversarial Networks)](https://github.com/fendouai/PyTorchDocs/blob/master/SixthSection/Dcgan.md)
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## 第七章:PyTorch之强化学习
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### 1.[强化学习(DQN)](https://github.com/fendouai/PyTorchDocs/blob/master/SeventhSection/ReinforcementLearning.md)
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# 教程推荐
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* PyTorch 入门教程
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[http://pytorchchina.com](http://pytorchchina.com)
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* 磐创AI 聊天机器人,智能客服:
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[http://www.panchuangai.com/](http://www.panchuangai.com/)
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* 磐创教程网站,TensorFlow,Pytorch,Keras:
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[http://panchuang.net/](http://panchuang.net/)
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* 魔图互联 知识图谱推荐系统:
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[http://motuhulian.com](http://motuhulian.com)
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由于译者水平有限,如有疏漏,欢迎提交 PR。

SecondSection/LICENSE

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MIT License
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Copyright (c) 2018 fendouai
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

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