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鍍金池/ 教程/ 人工智能/ BibTex 引用<a class="md-anchor" id="AUTOGENERATED-bibtex-citation"
BibTex 引用<a class="md-anchor" id="AUTOGENERATED-bibtex-citation"
術語表
自定義數(shù)據(jù)讀取 <a class="md-anchor" id="AUTOGENERATED-custom-data-reade
使用 GPUs <a class="md-anchor" id="AUTOGENERATED-using-gpus"></a>
Vector Representations of Words <a class="md-anchor" id="AUTOGEN
TensorFlow 個人學習心得
共享變量<a class="md-anchor" id="AUTOGENERATED-sharing-variables"></
應用實例 <a class="md-anchor" id="AUTOGENERATED-example-uses"></a>
其他資源 <a class="md-anchor" id="AUTOGENERATED-additional-resources
偏微分方程 <a class="md-anchor" id="AUTOGENERATED-partial-differentia
TensorBoard:可視化學習 <a class="md-anchor" id="AUTOGENERATED-tensorb
TensorFlow運作方式入門 <a class="md-anchor" id="AUTOGENERATED-tensorfl
常見問題 <a class="md-anchor" id="AUTOGENERATED-frequently-asked-que
MNIST機器學習入門 <a class="md-anchor" id="AUTOGENERATED-mnist-for-ml-
曼德布洛特(Mandelbrot)集合 <a class="md-anchor" id="AUTOGENERATED-mande
變量:創(chuàng)建、初始化、保存和加載
TensorBoard: 圖表可視化 <a class="md-anchor" id="AUTOGENERATED-tensor
簡介 <a class="md-anchor" id="AUTOGENERATED-introduction"></a>
張量的階、形狀、數(shù)據(jù)類型<a class="md-anchor" id="AUTOGENERATED-tensor-ranks-
線程和隊列 <a class="md-anchor" id="AUTOGENERATED-threading-and-queue
下載與安裝 <a class="md-anchor" id="AUTOGENERATED-download-and-setup"
常見問題匯總
綜述
綜述 Overview
TensorFlow 相關資源
數(shù)據(jù)讀取 <a class="md-anchor" id="AUTOGENERATED-reading-data"></a>
遞歸神經(jīng)網(wǎng)絡 <a class="md-anchor" id="AUTOGENERATED-recurrent-neural-n
深入MNIST <a class="md-anchor" id="AUTOGENERATED-deep-mnist-for-ex
增加一個新 Op <a class="md-anchor" id="AUTOGENERATED-adding-a-new-op"
卷積神經(jīng)網(wǎng)絡 <a class="md-anchor" id="AUTOGENERATED-convolutional-neur
基本使用 <a class="md-anchor" id="AUTOGENERATED-basic-usage"></a>
MNIST 數(shù)據(jù)下載 <a class="md-anchor" id="AUTOGENERATED-mnist-data-dow

BibTex 引用<a class="md-anchor" id="AUTOGENERATED-bibtex-citation"

如果你在研究中使用了 TensorFlow,并且希望引用 TensorFlow系統(tǒng)。我們建議你引用一下白皮書。

@misc{tensorflow2015-whitepaper,
title={{TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
url={http://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
    Dan~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},
}

文本形式如下:

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.

原文:http://tensorflow.org/resources/bib.md 翻譯:Jim-Zenn 校對:Wiki