Merge pull request #2 from MarkDaoust/import_docs_src

Import docs src

Author: MarkDaoust

site/en/BUILD

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+# Files used to generate TensorFlow docs.
+
+licenses(["notice"])  # Apache 2.0
+
+package(
+    default_visibility = ["//tensorflow:internal"],
+)
+
+exports_files(["LICENSE"])
+
+filegroup(
+    name = "docs_src",
+    data = glob(["**/*.md"]),
+)

site/en/__init__.py

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+# Attribution
+
+Please only use the TensorFlow name and marks when accurately referencing this
+software distribution, and do not use our marks in a way that suggests you are
+endorsed by or otherwise affiliated with Google. When referring to our marks,
+please include the following attribution statement: "TensorFlow, the TensorFlow
+logo and any related marks are trademarks of Google Inc."
+
+

site/en/about/attribution.md

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+# TensorFlow White Papers
+
+This document identifies white papers about TensorFlow.
+
+## Large-Scale Machine Learning on Heterogeneous Distributed Systems
+
+[Access this white paper.](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf)
+
+**Abstract:** TensorFlow is an interface for expressing machine learning
+algorithms, and an implementation for executing such algorithms.
+A computation expressed using TensorFlow can be
+executed with little or no change on a wide variety of heterogeneous
+systems, ranging from mobile devices such as phones
+and tablets up to large-scale distributed systems of hundreds
+of machines and thousands of computational devices such as
+GPU cards. The system is flexible and can be used to express
+a wide variety of algorithms, including training and inference
+algorithms for deep neural network models, and it has been
+used for conducting research and for deploying machine learning
+systems into production across more than a dozen areas of
+computer science and other fields, including speech recognition,
+computer vision, robotics, information retrieval, natural
+language processing, geographic information extraction, and
+computational drug discovery. This paper describes the TensorFlow
+interface and an implementation of that interface that
+we have built at Google. The TensorFlow API and a reference
+implementation were released as an open-source package under
+the Apache 2.0 license in November, 2015 and are available at
+www.tensorflow.org.
+
+
+### In BibTeX format
+
+If you use TensorFlow in your research and would like to cite the TensorFlow
+system, we suggest you cite this whitepaper.
+
+<pre>
+@misc{tensorflow2015-whitepaper,
+title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
+url={https://www.tensorflow.org/},
+note={Software available from tensorflow.org},
+author={
+    Mart\'{\i}n~Abadi and
+    Ashish~Agarwal and
+    Paul~Barham and
+    Eugene~Brevdo and
+    Zhifeng~Chen and
+    Craig~Citro and
+    Greg~S.~Corrado and
+    Andy~Davis and
+    Jeffrey~Dean and
+    Matthieu~Devin and
+    Sanjay~Ghemawat and
+    Ian~Goodfellow and
+    Andrew~Harp and
+    Geoffrey~Irving and
+    Michael~Isard and
+    Yangqing Jia and
+    Rafal~Jozefowicz and
+    Lukasz~Kaiser and
+    Manjunath~Kudlur and
+    Josh~Levenberg and
+    Dandelion~Man\'{e} and
+    Rajat~Monga and
+    Sherry~Moore and
+    Derek~Murray and
+    Chris~Olah and
+    Mike~Schuster and
+    Jonathon~Shlens and
+    Benoit~Steiner and
+    Ilya~Sutskever and
+    Kunal~Talwar and
+    Paul~Tucker and
+    Vincent~Vanhoucke and
+    Vijay~Vasudevan and
+    Fernanda~Vi\'{e}gas and
+    Oriol~Vinyals and
+    Pete~Warden and
+    Martin~Wattenberg and
+    Martin~Wicke and
+    Yuan~Yu and
+    Xiaoqiang~Zheng},
+  year={2015},
+}
+</pre>
+
+Or in textual form:
+
+<pre>
+Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo,
+Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis,
+Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow,
+Andrew Harp, Geoffrey Irving, Michael Isard, Rafal Jozefowicz, Yangqing Jia,
+Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Mike Schuster,
+Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Jonathon Shlens,
+Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker,
+Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas,
+Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke,
+Yuan Yu, and Xiaoqiang Zheng.
+TensorFlow: Large-scale machine learning on heterogeneous systems,
+2015. Software available from tensorflow.org.
+</pre>
+
+
+
+## TensorFlow: A System for Large-Scale Machine Learning
+
+[Access this white paper.](https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf)
+
+**Abstract:** TensorFlow is a machine learning system that operates at
+large scale and in heterogeneous environments. TensorFlow
+uses dataflow graphs to represent computation,
+shared state, and the operations that mutate that state. It
+maps the nodes of a dataflow graph across many machines
+in a cluster, and within a machine across multiple computational
+devices, including multicore CPUs, generalpurpose
+GPUs, and custom-designed ASICs known as
+Tensor Processing Units (TPUs). This architecture gives
+flexibility to the application developer: whereas in previous
+“parameter server” designs the management of shared
+state is built into the system, TensorFlow enables developers
+to experiment with novel optimizations and training algorithms.
+TensorFlow supports a variety of applications,
+with a focus on training and inference on deep neural networks.
+Several Google services use TensorFlow in production,
+we have released it as an open-source project, and
+it has become widely used for machine learning research.
+In this paper, we describe the TensorFlow dataflow model
+and demonstrate the compelling performance that TensorFlow
+achieves for several real-world applications.
+

site/en/about/bib.md

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+# About TensorFlow
+
+This section provides a few documents about TensorFlow itself,
+including the following:
+
+  * [TensorFlow in Use](../about/uses.md), which provides a link to our model zoo and
+    lists some popular ways that TensorFlow is being used.
+  * [TensorFlow White Papers](../about/bib.md), which provides abstracts of white papers
+    about TensorFlow.
+  * [Attribution](../about/attribution.md), which specifies how to attribute and refer
+    to TensorFlow.

site/en/about/index.md

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+index.md
+uses.md
+bib.md
+attribution.md

site/en/about/leftnav_files

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+# TensorFlow In Use
+
+This page highlights TensorFlow models in real world use.
+
+
+## Model zoo
+
+Please visit our collection of TensorFlow models in the
+[TensorFlow Zoo](https://github.com/tensorflow/models).
+
+If you have built a model with TensorFlow, please consider publishing it in
+the Zoo.
+
+
+## Current uses
+
+This section describes some of the current uses of the TensorFlow system.
+
+> If you are using TensorFlow for research, for education, or for production
+> usage in some product, we would love to add something about your usage here.
+> Please feel free to [email us](mailto:[email protected]) a brief
+> description of how you're using TensorFlow, or even better, send us a
+> pull request to add an entry to this file.
+
+* **Deep Speech**
+<ul>
+   <li>**Organization**: Mozilla</li>
+   <li> **Domain**: Speech Recognition</li>
+   <li> **Description**:  A TensorFlow implementation motivated by Baidu's Deep Speech architecture.</li>
+   <li> **More info**: [GitHub Repo](https://github.com/mozilla/deepspeech)</li>
+</ul>
+
+* **RankBrain**
+<ul>
+   <li>**Organization**: Google</li>
+   <li> **Domain**: Information Retrieval</li>
+   <li> **Description**:  A large-scale deployment of deep neural nets for search ranking on www.google.com.</li>
+   <li> **More info**: ["Google Turning Over Its Lucrative Search to AI Machines"](http://www.bloomberg.com/news/articles/2015-10-26/google-turning-its-lucrative-web-search-over-to-ai-machines)</li>
+</ul>
+
+* **Inception Image Classification Model**
+<ul>
+   <li> **Organization**: Google</li>
+   <li> **Description**: Baseline model and follow on research into highly accurate computer vision models, starting with the model that won the 2014 Imagenet image classification challenge</li>
+   <li> **More Info**: Baseline model described in [Arxiv paper](http://arxiv.org/abs/1409.4842)</li>
+</ul>
+
+* **SmartReply**
+<ul>
+  <li> **Organization**: Google</li>
+  <li> **Description**: Deep LSTM model to automatically generate email responses</li>
+  <li> **More Info**: [Google research blog post](http://googleresearch.blogspot.com/2015/11/computer-respond-to-this-email.html)</li>
+</ul>
+
+* **Massively Multitask Networks for Drug Discovery**
+<ul>
+  <li> **Organization**: Google and Stanford University</li>
+  <li> **Domain**: Drug discovery</li>
+  <li> **Description**:  A deep neural network model for identifying promising drug candidates.</li>
+  <li> **More info**: [Arxiv paper](http://arxiv.org/abs/1502.02072)</li>
+</ul>
+
+* **On-Device Computer Vision for OCR**
+<ul>
+  <li> **Organization**: Google</li>
+  <li> **Description**: On-device computer vision model to do optical character recognition to enable real-time translation.</li>
+  <li> **More info**: [Google Research blog post](http://googleresearch.blogspot.com/2015/07/how-google-translate-squeezes-deep.html)</li>
+</ul>