提交 77ddce0f 编写于 作者: Q qingqing01 提交者: GitHub

Merge pull request #526 from wangkuiyi/fix_readme

Encourage users to file Github issues instead of emails.
...@@ -14,7 +14,7 @@ developed by Baidu scientists and engineers for the purpose of applying deep ...@@ -14,7 +14,7 @@ developed by Baidu scientists and engineers for the purpose of applying deep
learning to many products at Baidu. learning to many products at Baidu.
Our vision is to enable deep learning for everyone via PaddlePaddle. Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our [release announcement](https://github.com/baidu/Paddle/releases) to track the latest feature of PaddlePaddle. Please refer to our [release announcement](https://github.com/baidu/Paddle/releases) to track the latest feature of PaddlePaddle.
## Features ## Features
...@@ -26,15 +26,15 @@ Please refer to our [release announcement](https://github.com/baidu/Paddle/relea ...@@ -26,15 +26,15 @@ Please refer to our [release announcement](https://github.com/baidu/Paddle/relea
connection. connection.
- **Efficiency** - **Efficiency**
In order to unleash the power of heterogeneous computing resource, In order to unleash the power of heterogeneous computing resource,
optimization occurs at different levels of PaddlePaddle, including optimization occurs at different levels of PaddlePaddle, including
computing, memory, architecture and communication. The following are some computing, memory, architecture and communication. The following are some
examples: examples:
- Optimized math operations through SSE/AVX intrinsics, BLAS libraries - Optimized math operations through SSE/AVX intrinsics, BLAS libraries
(e.g. MKL, ATLAS, cuBLAS) or customized CPU/GPU kernels. (e.g. MKL, ATLAS, cuBLAS) or customized CPU/GPU kernels.
- Highly optimized recurrent networks which can handle **variable-length** - Highly optimized recurrent networks which can handle **variable-length**
sequence without padding. sequence without padding.
- Optimized local and distributed training for models with high dimensional - Optimized local and distributed training for models with high dimensional
sparse data. sparse data.
...@@ -57,41 +57,39 @@ Please refer to our [release announcement](https://github.com/baidu/Paddle/relea ...@@ -57,41 +57,39 @@ Please refer to our [release announcement](https://github.com/baidu/Paddle/relea
## Installation ## Installation
Check out the [Install Guide](http://paddlepaddle.org/doc/build/) to install from Check out the [Install Guide](http://paddlepaddle.org/doc/build/) to install from
pre-built packages (**docker image**, **deb package**) or pre-built packages (**docker image**, **deb package**) or
directly build on **Linux** and **Mac OS X** from the source code. directly build on **Linux** and **Mac OS X** from the source code.
## Documentation ## Documentation
Both [English Docs](http://paddlepaddle.org/doc/) and [Chinese Docs](http://paddlepaddle.org/doc_cn/) are provided for our users and developers. 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> - [Quick Start](http://paddlepaddle.org/doc/demo/quick_start/index_en) <br>
You can follow the quick start tutorial to learn how use PaddlePaddle You can follow the quick start tutorial to learn how use PaddlePaddle
step-by-step. step-by-step.
- [Example and Demo](http://paddlepaddle.org/doc/demo/) <br> - [Example and Demo](http://paddlepaddle.org/doc/demo/) <br>
We provide five demos, including: image classification, sentiment analysis, We provide five demos, including: image classification, sentiment analysis,
sequence to sequence model, recommendation, semantic role labeling. sequence to sequence model, recommendation, semantic role labeling.
- [Distributed Training](http://paddlepaddle.org/doc/cluster) <br> - [Distributed Training](http://paddlepaddle.org/doc/cluster) <br>
This system supports training deep learning models on multiple machines This system supports training deep learning models on multiple machines
with data parallelism. with data parallelism.
- [Python API](http://paddlepaddle.org/doc/ui/) <br> - [Python API](http://paddlepaddle.org/doc/ui/) <br>
PaddlePaddle supports using either Python interface or C++ to build your PaddlePaddle supports using either Python interface or C++ to build your
system. We also use SWIG to wrap C++ source code to create a user friendly system. We also use SWIG to wrap C++ source code to create a user friendly
interface for Python. You can also use SWIG to create interface for your interface for Python. You can also use SWIG to create interface for your
favorite programming language. favorite programming language.
- [How to Contribute](http://paddlepaddle.org/doc/build/contribute_to_paddle.html) <br> - [How to Contribute](http://paddlepaddle.org/doc/build/contribute_to_paddle.html) <br>
We sincerely appreciate your interest and contributions. If you would like to We sincerely appreciate your interest and contributions. If you would like to
contribute, please read the contribution guide. contribute, please read the contribution guide.
- [Source Code Documents](http://paddlepaddle.org/doc/source/) <br> - [Source Code Documents](http://paddlepaddle.org/doc/source/) <br>
## Ask Questions ## Ask Questions
Please join the [**gitter chat**](https://gitter.im/PaddlePaddle/Deep_Learning) or send email to
**paddle-dev@baidu.com** to ask questions and talk about methods and models. You are welcome to submit questions and bug reports as [Github Issues](https://github.com/baidu/paddle/issues).
Framework development discussions and
bug reports are collected on [Issues](https://github.com/baidu/paddle/issues).
## Copyright and License ## Copyright and License
PaddlePaddle is provided under the [Apache-2.0 license](LICENSE). PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).
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