revised readme/readme_cn for paddle (#22219)

* these are the revised readme/readme_cn pull-request, test=develop

* this is a readme pull request, test = develop

* these are the revised readme/readme_cn pull request,test = develop

README.md

+
 # PaddlePaddle
 
 English | [简体中文](./README_cn.md)
@@ -10,15 +11,17 @@ English | [简体中文](./README_cn.md)
 
 Welcome to the PaddlePaddle GitHub.
 
-PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use,
-efficient, flexible and scalable deep learning platform, which is originally
-developed by Baidu scientists and engineers for the purpose of applying deep
-learning to many products at Baidu.
+PaddlePaddle, as the only independent R&D deep learning platform in China, has been officially open-sourced to professional communities since 2016. It is an industrial platform with advanced technologies and rich features that cover core deep learning frameworks, basic model libraries, end-to-end development kits, tool & component as well as service platforms.
+PaddlePaddle is originated from industrial practices with dedication and commitments to industrialization. It has been widely adopted by a wide range of sectors including manufacturing, agriculture, enterprise service and so on while serving more than 1.5 million developers. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI.
 
-Our vision is to enable deep learning for everyone via PaddlePaddle.
-Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle.
+
+
+## Installation
 
 ### Latest PaddlePaddle Release: [v1.6](https://github.com/PaddlePaddle/Paddle/tree/release/1.6)
+
+Our vision is to enable deep learning for everyone via PaddlePaddle.
+Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle.
 ### Install Latest Stable Release:
 ```
 # Linux CPU
@@ -28,72 +31,61 @@ pip install paddlepaddle-gpu
 # Linux GPU cuda9cudnn7
 pip install paddlepaddle-gpu==1.6.3.post97
 
-
-# For installation on other platform, refer to http://paddlepaddle.org/
 ```
+It is recommended to read [this doc](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/install/index_en.html) on our website.
+
 Now our developers could acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you would obtain 12 hours to train models online per day. If you could insist on that for five consecutive days, then you would own extra 48 hours. [Click here to start](http://ai.baidu.com/support/news?action=detail&id=981).
 
-## Features
+## FOUR LEADING TECHNOLOGIES
 
-- **Flexibility**
+- **Agile Framework for Industrial Development of Deep Neural Networks**
 
-    PaddlePaddle supports a wide range of neural network architectures and
-    optimization algorithms. It is easy to configure complex models such as
-    neural machine translation model with attention mechanism or complex memory
-    connection.
+    The PaddlePaddle deep learning framework facilitates the development while lowering the technical burden,through leveraging a programmable scheme to architect the neural networks. It supports both declarative programming and imperative programming with both development flexibility and high runtime performance preserved.  The neural architectures could be automatically designed by algorithms with better performance than the ones designed by human experts.
 
--  **Efficiency**
 
-    In order to unleash the power of heterogeneous computing resource,
-    optimization occurs at different levels of PaddlePaddle, including
-    computing, memory, architecture and communication. The following are some
-    examples:
+-  **Support Ultra-Large-Scale Training of Deep Neural Networks**
 
-      - Optimized math operations through SSE/AVX intrinsics, BLAS libraries
-      (e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
-      - Optimized CNN networks through MKL-DNN library.
-      - Highly optimized recurrent networks which can handle **variable-length**
-      sequence without padding.
-      - Optimized local and distributed training for models with high dimensional
-      sparse data.
+    PaddlePaddle has made breakthroughs in ultra-large-scale deep neural networks training. It launched the world's first large-scale open source training platform that supports the deep networks training with 100 billions of features and trillions of parameters using data sources distributed over hundreds of nodes. PaddlePaddle overcomes the online deep learning challenges for ultra-large-scale deep learning models, and further achieved  the real-time model updating with more than 1 trillion parameters.
+     [Click here to learn more](https://github.com/PaddlePaddle/Fleet)
 
-- **Scalability**
 
-    With PaddlePaddle, it is easy to use many CPUs/GPUs and machines to speed
-    up your training. PaddlePaddle can achieve high throughput and performance
-    via optimized communication.
+- **Accelerated High-Performance Inference over Ubiquitous Deployments**
 
-- **Connected to Products**
+    PaddlePaddle is not only compatible with other open-source frameworks for models training, but also works well on the ubiquitous developments, varying from platforms to devices. More specific, PaddlePaddle accelerates the inference procedure with fastest speed-up. Note that, a recent breakthrough of inference speed has been made by PaddlePaddle on Huawei's Kirin NPU, through the hardware/software co-optimization.
+     [Click here to learn more](https://github.com/PaddlePaddle/Paddle-Lite)
      
-    In addition, PaddlePaddle is also designed to be easily deployable. At Baidu,
-    PaddlePaddle has been deployed into products and services with a vast number
-    of users, including ad click-through rate (CTR) prediction, large-scale image
-    classification, optical character recognition(OCR), search ranking, computer
-    virus detection, recommendation, etc. It is widely utilized in products at
-    Baidu and it has achieved a significant impact. We hope you can also explore
-    the capability of PaddlePaddle to make an impact on your product.
      
-## Installation
+- **Industry-Oriented Models and Libraries with Open Source Repositories**
+
+     PaddlePaddle includes and maintains more than 100 mainstream models that have been practiced and polished for a long time in industry. Some of these models have won major prizes from key international competitions. In the meanwhile, PaddlePaddle has further more than 200 pre-training models (some of them with source codes) to facilitate the rapid development of industrial applications.
+     [Click here to learn more](https://github.com/PaddlePaddle/models)
      
-It is recommended to read [this doc](http://www.paddlepaddle.org.cn/documentation/docs/en/1.6/beginners_guide/index_en.html) on our website.
 
 ## Documentation
 
 We provide [English](http://www.paddlepaddle.org.cn/documentation/docs/en/1.6/beginners_guide/index_en.html) and
-[Chinese](http://www.paddlepaddle.org.cn/documentation/docs/zh/1.6/beginners_guide/install/index_cn.html) documentation.
+[Chinese](http://www.paddlepaddle.org.cn/documentation/docs/zh/1.6/beginners_guide/index_cn.html) documentation.
+
+- [Basic Deep Learning Models](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/basics/index_en.html#basic-deep-learning-models)
+
+  You might want to start from how to implement deep learning basics with PaddlePaddle.
 
-- [Deep Learning 101](https://github.com/PaddlePaddle/book)
 
-  You might want to start from this online interactive book that can run in a Jupyter Notebook.
+- [User Guides](https://www.paddlepaddle.org.cn/documentation/docs/en/user_guides/index_en.html)
 
-- [Distributed Training](http://paddlepaddle.org.cn/documentation/docs/en/1.6/user_guides/howto/training/multi_node_en.html)
+  You might have got the hang of Beginner’s Guide, and wish to model practical problems and build your original networks.
   
-  You can run distributed training jobs on MPI clusters.
   
-- [Python API](http://paddlepaddle.org.cn/documentation/docs/en/1.6/api/index_en.html)
+- [Advanced User Guides](https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_usage/index_en.html)
+
+  So far you have already been familiar with Fluid. And the next expectation should be building a more efficient model or inventing your original Operator. 
+
+
+- [API Reference](https://www.paddlepaddle.org.cn/documentation/docs/en/api/index_en.html)
 
    Our new API enables much shorter programs.
 
+
 - [How to Contribute](http://paddlepaddle.org.cn/documentation/docs/en/1.6/advanced_usage/development/contribute_to_paddle/index_en.html)
 
    We appreciate your contributions!

README_cn.md

+
 # PaddlePaddle
 
 [English](./README.md) | 简体中文
@@ -10,13 +11,14 @@
 
 欢迎来到 PaddlePaddle GitHub
 
-PaddlePaddle (PArallel Distributed Deep LEarning) 是一个简单易用、高效灵活、可扩展的深度学习平台，最初由百度科学家和工程师共同开发，目的是将深度学习技术应用到百度的众多产品中。
+飞桨（飞桨（PaddlePaddle）是目前国内唯一自主研发、开源开放、功能完备的产业级深度学习平台，集深度学习核心框架、基础模型库、端到端开发套件、工具组件和服务平台于一体。飞桨源于产业实践，致力于与产业深入融合，提供了领先的深度学习&机器学习任务开发、训练、部署能力，加速企业从算法研发到产业落地的过程。目前飞桨已广泛应用于工业、农业、服务业等，服务150多万开发者，与合作伙伴一起帮助越来越多的行业完成AI赋能。
 
-我们的愿景是让每个人都能通过PaddlePaddle接触深度学习
+
+## 安装
+### PaddlePaddle最新版本: [v1.6](https://github.com/PaddlePaddle/Paddle/tree/release/1.6)
 
 跟进PaddlePaddle最新特性请参考我们的[版本说明](https://github.com/PaddlePaddle/Paddle/releases)
 
-### PaddlePaddle最新版本: [v1.6](https://github.com/PaddlePaddle/Paddle/tree/release/1.6)
 ### 安装最新稳定版本:
 ```
 # Linux CPU
@@ -26,56 +28,61 @@ pip install paddlepaddle-gpu
 # Linux GPU cuda9cudnn7
 pip install paddlepaddle-gpu==1.6.3.post97
 
-
-# 其他平台上的安装指引请参考 http://paddlepaddle.org/
 ```
+更多安装信息详见官网 [安装说明](http://www.paddlepaddle.org.cn/documentation/docs/zh/1.6/beginners_guide/install/index_cn.html)
+
 PaddlePaddle用户可领取**免费Tesla V100在线算力资源**，训练模型更高效。**每日登陆即送12小时**，**连续五天运行再加送48小时**，[前往使用免费算力](https://ai.baidu.com/support/news?action=detail&id=981)。
 
-## 特性
+## 四大领先技术
 
-- **灵活性**
+- **开发便捷的产业级深度学习框架**
 
-    PaddlePaddle支持丰富的神经网络架构和优化算法。易于配置复杂模型，例如带有注意力机制或复杂记忆连接的神经网络机器翻译模型。
+    飞桨深度学习框架采用基于编程逻辑的组网范式，对于普通开发者而言更容易上手，符合他们的开发习惯。同时支持声明式和命令式编程，兼具开发的灵活性和高性能。网络结构自动设计，模型效果超越人类专家。
     
--  **高效性**
 
-    为了高效使用异步计算资源，PaddlePaddle对框架的不同层进行优化，包括计算、存储、架构和通信。下面是一些样例：
+- **支持超大规模深度学习模型的训练**
 
-    - 通过SSE/AVX 内置函数、BLAS库(例如MKL、OpenBLAS、cuBLAS)或定制的CPU/GPU内核优化数学操作。
-    - 通过MKL-DNN库优化CNN网络
-    - 高度优化循环网络，无需执行 `padding` 操作即可处理 **变长** 序列
-    - 针对高维稀疏数据模型，优化了局部和分布式训练。
+    飞桨突破了超大规模深度学习模型训练技术，实现了支持千亿特征、万亿参数、数百节点的开源大规模训练平台，攻克了超大规模深度学习模型的在线学习难题，实现了万亿规模参数模型的实时更新。
+    [查看详情](https://github.com/PaddlePaddle/Fleet)
     
 
-- **稳定性**
+- **多端多平台部署的高性能推理引擎**
 
-    有了 PaddlePaddle，使得利用各种CPU/GPU和机器来加速训练变得简单。PaddlePaddle 通过优化通信可以实现巨大吞吐量和快速执行。
+    飞桨不仅兼容其他开源框架训练的模型，还可以轻松地部署到不同架构的平台设备上。同时，飞桨的推理速度也是全面领先的。尤其经过了跟华为麒麟NPU的软硬一体优化，使得飞桨在NPU上的推理速度进一步突破。
+    [查看详情](https://github.com/PaddlePaddle/Paddle-Lite)
 
-- **与产品相连**
 
-    另外，PaddlePaddle 的设计也易于部署。在百度，PaddlePaddle 已经部署到含有巨大用户量的产品和服务上，包括广告点击率（CTR）预测、大规模图像分类、光学字符识别（OCR）、搜索排序，计算机病毒检测、推荐系统等等。PaddlePaddle广泛应用于百度产品中，产生了非常重要的影响。我们希望您也能探索 PaddlePaddle 的能力，为您的产品创造新的影响力和效果。
+- **面向产业应用，开源开放覆盖多领域的工业级模型库。**
 
-## 安装
+    飞桨官方支持100多个经过产业实践长期打磨的主流模型，其中包括在国际竞赛中夺得冠军的模型；同时开源开放200多个预训练模型，助力快速的产业应用。
+    [查看详情](https://github.com/PaddlePaddle/models)
 
-推荐阅读官网上的[安装说明](http://www.paddlepaddle.org.cn/documentation/docs/zh/1.6/beginners_guide/install/index_cn.html)
 
 ## 文档
 
-我们提供[英文](http://www.paddlepaddle.org.cn/documentation/docs/en/1.6/beginners_guide/index_en.html)和
-[中文](http://www.paddlepaddle.org.cn/documentation/docs/zh/1.6/beginners_guide/install/index_cn.html) 文档
+我们提供 [英文](http://www.paddlepaddle.org.cn/documentation/docs/en/1.6/beginners_guide/index_en.html) 和
+[中文](http://www.paddlepaddle.org.cn/documentation/docs/zh/1.6/beginners_guide/index_cn.html) 文档
 
-- [深度学习101](https://github.com/PaddlePaddle/book)
+- [深度学习基础教程](https://www.paddlepaddle.org.cn/documentation/docs/zh/1.6/beginners_guide/basics/index_cn.html)
 
-  或许您想从这个在线交互式书籍开始，可以在Jupyter Notebook中运行
+   或许您想从深度学习基础开始学习飞桨
   
-- [分布式训练](http://paddlepaddle.org.cn/documentation/docs/zh/1.6/user_guides/howto/training/multi_node.html)
 
-  可以在MPI集群上运行分布式训练任务
+- [使用指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/1.6/user_guides/index_cn.html)
 
-- [Python API](http://paddlepaddle.org.cn/documentation/docs/zh/1.6/api_cn/index_cn.html)
+   或许您已经掌握了新手入门阶段的内容，期望可以针对实际问题建模、搭建自己网络
+  
+
+- [进阶使用](https://www.paddlepaddle.org.cn/documentation/docs/zh/1.6/advanced_usage/index_cn.html)
+
+   或许您已比较熟练使用PaddlePaddle来完成常规任务，期望获得更高效的模型或者定义自己的Operator
+  
+  
+- [API Reference](http://paddlepaddle.org.cn/documentation/docs/zh/1.6/api_cn/index_cn.html)
 
    新的API支持代码更少更简洁的程序
    
+
 - [贡献方式](http://paddlepaddle.org.cn/documentation/docs/zh/1.6/advanced_usage/development/contribute_to_paddle/index_cn.html)
 
    欢迎您的贡献!