Over the past few years, we have built large-scale computer systems for training neural networks and then applied these systems to a wide variety of problems that have traditionally been very difficult for computers. We have made significant improvements in the state-of-the-art in many of these areas and our software systems and algorithms have been used by dozens of different groups at Google to train state-of-the-art models for speech recognition, image recognition, various visual detection tasks, language modeling, language translation, and many other tasks. In this talk, Google Senior Fellow Jeff Dean highlights some of the distributed systems and algorithms that Google uses in order to train large models quickly. He also discusses ways Google has applied this work to a variety of problems in its products, usually in close collaboration with other teams. Jeff Dean, senior fellow, Google Knowledge Group 02/26/2015 www.cs.washington.edu/events/colloquia/details?id=2668 uwtv.org

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