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