Large-Scale Deep Learning For Building Intelligent Computer Systems

J. Dean
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引用次数: 16

Abstract

For the past five years, the Google Brain team has focused on conducting research in difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to dozens of Google products. Our group has recently open-sourced the TensorFlow system (tensorflow.org), a system designed to easily express machine ideas, and to quickly train, evaluate and deploy machine learning systems. In this talk, I'll highlight some of the design decisions we made in building TensorFlow, discuss research results produced within our group, and describe ways in which these ideas have been applied to a variety of problems in Google's products, usually in close collaboration with other teams. This talk describes joint work with many people at Google.
构建智能计算机系统的大规模深度学习
在过去的五年里,谷歌大脑团队专注于研究人工智能中的难题,为机器学习研究构建大型计算机系统,并与谷歌的许多团队合作,将我们的研究和系统应用于数十种谷歌产品。我们的团队最近开源了TensorFlow系统(tensorflow.org),该系统旨在轻松表达机器思想,并快速训练,评估和部署机器学习系统。在这次演讲中,我将重点介绍我们在构建TensorFlow时所做的一些设计决策,讨论我们小组内产生的研究成果,并描述这些想法如何应用于谷歌产品中的各种问题,通常是与其他团队密切合作。这次演讲描述了与谷歌许多人的共同工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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