Simply put, they are the coding equivalent of Chuck Norris-style jokes (for example, 'Chuck Norris can slam a revolving door'). This talk describes joint work with many people at Google. About This Repo The 'Jeff Dean facts' are a set of jokes that revolve around the extraordinary programming prowess of their titular Google employee.
He will then discuss ways in which Google has applied this work to a variety of problems in Google's products, usually in close collaboration with other teams. In this talk, Jeff highlights some of ways that Google trains large models quickly on large datasets, and discusses different approaches for deploying machine learning models in environments ranging from large datacenters to mobile devices. Using TensorFlow, Google's research group has made significant improvements in the state-of-the-art in many areas, and dozens of different groups at Google use it to train state-of-the-art models for speech recognition, image recognition, various visual detection tasks, language modeling, language translation, and many other tasks. Google has released its second generation system, TensorFlow, as an open source project, and is now collaborating with a growing community on improving and extending its functionality.
Over the past few years, Google has built two generations of large-scale computer systems for training neural networks, and then applied these systems to a wide variety of research problems that have traditionally been very difficult for computers. Jeff Dean, Senior Fellow at Google, presents the "Large-Scale Deep Learning for Building Intelligent Computer Systems" keynote at the May 2016 Embedded Vision Summit.
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