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caddc5c6
编写于
6月 30, 2017
作者:
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<!DOCTYPE html>
<html
lang=
"en"
>
<head>
<meta
charset=
"UTF-8"
>
<title>
PaddlePaddle
</title>
<link
rel=
"stylesheet"
href=
"./css/home.css"
>
<!-- Baidu Analystics -->
<script>
var
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[];
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{
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hm
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</head>
<body>
<header
class=
"site-header"
>
<nav
class=
"row"
>
<div
class=
"logo"
>
<img
src=
"./images/logo.png"
>
</div>
<nav
class=
"top-nav"
>
<ul
class=
"site-links"
>
<li><a
class=
"active"
>
主页
</a></li>
<li><a
href=
"http://book.paddlepaddle.org/index.cn.html"
target=
"_blank"
>
快速开始
</a></li>
<li><a
href=
"http://www.paddlepaddle.org/doc_cn/howto/index_cn.html"
target=
"_blank"
>
文档中心
</a></li>
<li
class=
"version-switcher"
>
<a>
版本
<i
class=
"fa"
aria-hidden=
"true"
></i></a>
<ul>
<li><a
href=
"http://www.paddlepaddle.org/release/0.10.0/doc_cn/"
target=
"_blank"
>
0.10.0
</a></li>
<li><a
href=
"http://www.paddlepaddle.org/release_doc/0.9.0/doc_cn/"
target=
"_blank"
>
0.9.0
</a></li>
</ul>
</li>
</ul>
</nav>
<nav
class=
"right-nav"
>
<div
class=
"language-switcher"
>
<a
href=
"./index.html"
>
English
</a>
</div>
<div
class=
"github-fork"
>
<a
href=
"https://github.com/PaddlePaddle/Paddle"
target=
"_blank"
>
<i
class=
"fa fa-github"
aria-hidden=
"true"
></i>
<span>
Github
</span>
</a>
</div>
</nav>
</nav>
</header>
<section
class=
"head-banner"
>
<div
class=
"row banner"
>
<h1>
易学易用的分布式深度学习平台
</h1>
<p>
正在为100+项产品提供深度学习算法支持
</p>
<div>
<a
class=
"quick-start"
href=
"http://book.paddlepaddle.org/index.cn.html"
target=
"_blank"
>
快速入门
</a>
</div>
</div>
</section>
<section
class=
"services"
>
<div
class=
"row"
>
<h2><span>
丰富的算法服务
</span></h2>
<p
class=
"sub-title"
>
易用、高效、灵活、扩展性好
</p>
</div>
<div
class=
"row"
>
<div>
<img
class=
"service-icon"
src=
"./images/service-1.png"
>
</div>
<div>
<div
class=
"service-desc"
>
<h3>
机器视觉
</h3>
<p>
卷积神经网络可以识别图像中的主要对象,并输出分类结果
</p>
<div>
<a
role=
"button"
class=
"view-more"
href=
"http://book.paddlepaddle.org/03.image_classification/index.cn.html"
target=
"_blank"
>
查看更多 >
</a>
</div>
</div>
</div>
</div>
<div
class=
"row"
>
<div>
<div
class=
"service-desc"
>
<h3>
自然语言理解
</h3>
<p>
利用LSTM网络从IMDB电影评论的中分析出评论者情绪的正面和负面
</p>
<div>
<a
role=
"button"
class=
"view-more"
href=
"http://book.paddlepaddle.org/06.understand_sentiment/index.cn.html"
target=
"_blank"
>
查看更多 >
</a>
</div>
</div>
</div>
<div>
<img
class=
"service-icon"
src=
"./images/service-2.png"
>
</div>
</div>
<div
class=
"row"
>
<div>
<img
class=
"service-icon"
src=
"./images/service-3.png"
>
</div>
<div>
<div
class=
"service-desc"
>
<h3>
搜索引擎排序
</h3>
<p>
分析用户特征、电影特征、点评分数,预测新用户对不同电影的点评分数
</p>
<div>
<a
role=
"button"
class=
"view-more"
href=
"http://book.paddlepaddle.org/05.recommender_system/index.cn.html"
target=
"_blank"
>
查看更多 >
</a>
</div>
</div>
</div>
</div>
</section>
<section
class=
"features"
>
<div
class=
"row"
>
<h2><span>
技术和服务优势
</span></h2>
</div>
<div
class=
"row"
>
<div
class=
"feature-desc"
>
<div
class=
"feature-icon"
>
<img
src=
"./images/feature-1.png"
>
</div>
<h3>
易用性
</h3>
<p>
为用户提供了直观、灵活的数据接口和模型配置接口
</p>
</div>
<div
class=
"feature-desc"
>
<div
class=
"feature-icon"
>
<img
src=
"./images/feature-2.png"
>
</div>
<h3>
灵活性
</h3>
<p>
支持CNN、RNN等多种神经网络结构和优化算法。简单书写配置文件即可实现复杂模型
</p>
</div>
<div
class=
"feature-desc"
>
<div
class=
"feature-icon"
>
<img
src=
"./images/feature-3.png"
>
</div>
<h3>
高效性
</h3>
<p>
在计算、存储、通信、架构等方面都做了高效优化，充分发挥各种资源的性能
</p>
</div>
<div
class=
"feature-desc"
>
<div
class=
"feature-icon"
>
<img
src=
"./images/feature-4.png"
>
</div>
<h3>
扩展性
</h3>
<p>
全面支持多核、多GPU、多机环境。轻松应对大规模数据训练需求
</p>
</div>
</div>
</section>
<section
class=
"get-started"
>
<div
class=
"row"
>
<h2>
现在开始使用PaddlePaddle
</h2>
<p>
易学易用的分布式深度学习平台
</p>
<div>
<a
role=
"button"
class=
"quick-start"
href=
"http://book.paddlepaddle.org/index.cn.html"
target=
"_blank"
>
快速入门
</a>
</div>
</div>
</section>
<footer
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"footer-nav"
>
<div
class=
"row"
>
<div
class=
"row"
>
<p
class=
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>
©
Copyright
2017,
PaddlePaddle
developers.
</p>
</div>
</footer>
<script
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"./js/common.bundle.js"
></script>
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></script>
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<link
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>
<script>
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<img
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<img
src=
data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEYAAABGCAYAAABxLuKEAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAyhpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADw/eHBhY2tldCBiZWdpbj0i77u/IiBpZD0iVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkIj8+IDx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IkFkb2JlIFhNUCBDb3JlIDUuNi1jMTM4IDc5LjE1OTgyNCwgMjAxNi8wOS8xNC0wMTowOTowMSAgICAgICAgIj4gPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4gPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIgeG1sbnM6eG1wPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvIiB4bWxuczp4bXBNTT0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wL21tLyIgeG1sbnM6c3RSZWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9zVHlwZS9SZXNvdXJjZVJlZiMiIHhtcDpDcmVhdG9yVG9vbD0iQWRvYmUgUGhvdG9zaG9wIENDIDIwMTcgKE1hY2ludG9zaCkiIHhtcE1NOkluc3RhbmNlSUQ9InhtcC5paWQ6RTk2QTdBMTA1Mzc4MTFFN0E4REJBRkE1QTg3REJFMTEiIHhtcE1NOkRvY3VtZW50SUQ9InhtcC5kaWQ6RTk2QTdBMTE1Mzc4MTFFN0E4REJBRkE1QTg3REJFMTEiPiA8eG1wTU06RGVyaXZlZEZyb20gc3RSZWY6aW5zdGFuY2VJRD0ieG1wLmlpZDpFOTZBN0EwRTUzNzgxMUU3QThEQkFGQTVBODdEQkUxMSIgc3RSZWY6ZG9jdW1lbnRJRD0ieG1wLmRpZDpFOTZBN0EwRjUzNzgxMUU3QThEQkFGQTVBODdEQkUxMSIvPiA8L3JkZjpEZXNjcmlwdGlvbj4gPC9yZGY6UkRGPiA8L3g6eG1wbWV0YT4gPD94cGFja2V0IGVuZD0iciI/Ph6EFjsAAAGLSURBVHja7Ns9bsIwGMZxzMDWofQ4TU9AuUVnOEPVpd2aC7Ew9ixkaFmYwhvpRYootBb+iO38LT0KihCKf8LfYNq2nVB+FwMMMMAAA0yGMMaYrAGu1R+YgmG2en0C5qxup0cBBhhggAEGGGBGBTPNfEkz1+ve9wfnDvOi168gawWXDFTuJSvJQZvS0nf9U4R5lmwkP1rp//IW4ouRGsyrJcZe8ZahWkxKMAut9EGbyTzWtkPqMBuFWcfej7mUlOYx35I7yYOkYaPK00QNGGCAAQYYYMqFmVpUfCapJTtJex7LKbxN+kAhs5PUkpnT6lrKZ4SHHSLvTksCFe5eVAltO7iUqvfNuX1JoM2le7NJdGvTqT+7ufMFBhhggIkNc7p3YUKW231T6rlSsEJToo8BBhhGJUYlRiX6GGCAASbUqOStf2RUYlSijwEGGGDGAWMzKjUKUBUy4Dz263WdbrxHtB9/1d8Gpjv8rlW4BJBm0jvUz+FXm8N0NvzvOjLMaJcEwFCAAQYY/+UowACnJKfry2DPVAAAAABJRU5ErkJggg==
>
</div>
<h3>
易用性
</h3>
<p>
为用户提供了直观、灵活的数据接口和模型配置接口
</p>
</div>
<div
class=
feature-desc
>
<div
class=
feature-icon
>
<img
src=
data:image/png;base64,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
>
</div>
<h3>
灵活性
</h3>
<p>
支持CNN、RNN等多种神经网络结构和优化算法。简单书写配置文件即可实现复杂模型
</p>
</div>
<div
class=
feature-desc
>
<div
class=
feature-icon
>
<img
src=
data:image/png;base64,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
>
</div>
<h3>
高效性
</h3>
<p>
在计算、存储、通信、架构等方面都做了高效优化，充分发挥各种资源的性能
</p>
</div>
<div
class=
feature-desc
>
<div
class=
feature-icon
>
<img
src=
data:image/png;base64,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
>
</div>
<h3>
扩展性
</h3>
<p>
全面支持多核、多GPU、多机环境。轻松应对大规模数据训练需求
</p>
</div>
</div>
</section>
<section
class=
get-started
>
<div
class=
row
>
<h2>
现在开始使用PaddlePaddle
</h2>
<p>
易学易用的分布式深度学习平台
</p>
<div>
<a
role=
button
class=
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Efficient optimization of computing, memory, communications and architecture.
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Scalability
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Easy to use many CPUs/GPUs and machines to speed up your training and handle large-scale data easily.
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<img
src=
data:image/png;base64,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
>
</div>
<h3>
Ease of use
</h3>
<p>
Provids an intuitive and flexible interface for loading data and specifying model structure.
</p>
</div>
<div
class=
feature-desc
>
<div
class=
feature-icon
>
<img
src=
data:image/png;base64,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
>
</div>
<h3>
Flexibility
</h3>
<p>
Supports CNN, RNN and other neural network. Easy to configure complex models.
</p>
</div>
<div
class=
feature-desc
>
<div
class=
feature-icon
>
<img
src=
data:image/png;base64,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
>
</div>
<h3>
Efficiency
</h3>
<p>
Efficient optimization of computing, memory, communications and architecture.
</p>
</div>
<div
class=
feature-desc
>
<div
class=
feature-icon
>
<img
src=
data:image/png;base64,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
>
</div>
<h3>
Scalability
</h3>
<p>
Easy to use many CPUs/GPUs and machines to speed up your training and handle large-scale data easily.
</p>
</div>
</div>
</section>
<section
class=
get-started
>
<div
class=
row
>
<h2>
Start Using PaddlePaddle
</h2>
<p>
Easy to Learn and Use Distributed Deep Learning Platform
</p>
<div>
<a
role=
button
class=
quick-start
href=
http://book.paddlepaddle.org/index.html
target=
_blank
>
Quick Start
</a>
</div>
</div>
</section>
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footer-nav
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<p
class=
copyright
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