Update Readme

README.md

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 # PaddlePaddle
-[![Build Status](https://travis-ci.org/baidu/Paddle.svg?branch=master)](https://travis-ci.org/baidu/Paddle)
 
-Welcome to the PaddlePaddle GitHub.
-
-Do you wanna try and play PaddlePaddle? Just following the [Install Guide](http://www.paddlepaddle.org/doc/build/index.html) and [Quick Start](http://www.paddlepaddle.org/doc/demo/quick_start/index_en.html). The chinese version is [Install Guide](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html) and [Quick Start](http://www.paddlepaddle.org/doc_cn/demo/quick_start/index.html).
+|  **`Linux`**   |
+|----------------|
+|[![Build Status](https://travis-ci.org/baidu/Paddle.svg?branch=master)](https://travis-ci.org/baidu/Paddle)|
 
-Please refer to our [release log](https://github.com/baidu/Paddle/releases) to track the latest feature of PaddlePaddle. 
+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.
 
+Our vision is to enable deep learning for everyone via PaddlePaddle.
+Please refer to our [release log](https://github.com/baidu/Paddle/releases) to track the latest feature of PaddlePaddle. 
+
 ## Features
 
 - **Flexibility**
 
-   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.
+    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.
 
 -  **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:
-  1. Optimized math operations through SSE/AVX intrinsics, BLAS libraries
-  (e.g. MKL, ATLAS, cuBLAS) or customized CPU/GPU kernels. 
-  2. Highly optimized recurrent networks which can handle **variable-length** 
-  sequence without padding.
-  3. Optimized local and distributed training for models with high dimensional
-  sparse data.
+    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:
+
+      - Optimized math operations through SSE/AVX intrinsics, BLAS libraries
+      (e.g. MKL, ATLAS, cuBLAS) or customized CPU/GPU kernels. 
+      - Highly optimized recurrent networks which can handle **variable-length** 
+      sequence without padding.
+      - Optimized local and distributed training for models with high dimensional
+      sparse data.
 
 - **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.
+    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.
 
 - **Connected to Products**
 
-  In addition, PaddlePaddle is also designed to be easily deployable. At Baidu,
-  PaddlePaddle has been deployed into products or service 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 exploit
-  the capability of PaddlePaddle to make a huge impact for your product.
+    In addition, PaddlePaddle is also designed to be easily deployable. At Baidu,
+    PaddlePaddle has been deployed into products or service 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 exploit
+    the capability of PaddlePaddle to make a huge impact for your product.
 
 ## Installation
-See [Installation Guide](http://paddlepaddle.org/doc/build/) to install from pre-built package or build from the source code. (Note: The installation packages are still in pre-release state and your experience of installation may not be smooth.).
-
+Check out the [Install Guide](http://paddlepaddle.org/doc/build/) to install from
+pre-built packages (**docker image**, **deb package**) or 
+directly build on **Linux** and **Mac OS X** from the source code.
+
 ## Documentation
-- [Chinese Documentation](http://paddlepaddle.org/doc_cn/) <br>
+Both [English Docs](http://paddlepaddle.org/doc/) and [Chinese Docs](http://paddlepaddle.org/doc_cn/) are provided for our users and developers.
 
 - [Quick Start](http://paddlepaddle.org/doc/demo/quick_start/index_en) <br>
    You can follow the quick start tutorial to learn how use PaddlePaddle