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@@ -0,0 +1,14 @@
# 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"]),
)
No changes.
@@ -0,0 +1,9 @@
# 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."


@@ -0,0 +1,131 @@
# 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.

@@ -0,0 +1,11 @@
# 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.
@@ -0,0 +1,4 @@
index.md
uses.md
bib.md
attribution.md
@@ -0,0 +1,68 @@
# 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:usecases@tensorflow.org) 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>
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