TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

14 Mar 2016Martín AbadiAshish AgarwalPaul BarhamEugene BrevdoZhifeng ChenCraig CitroGreg S. CorradoAndy DavisJeffrey DeanMatthieu DevinSanjay GhemawatIan GoodfellowAndrew HarpGeoffrey IrvingMichael IsardYangqing JiaRafal JozefowiczLukasz KaiserManjunath KudlurJosh LevenbergDan ManeRajat MongaSherry MooreDerek MurrayChris OlahMike SchusterJonathon ShlensBenoit SteinerIlya SutskeverKunal TalwarPaul TuckerVincent VanhouckeVijay VasudevanFernanda ViegasOriol VinyalsPete WardenMartin WattenbergMartin WickeYuan YuXiaoqiang Zheng

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... (read more)

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