In 2016, TensorFlow went from being an early release to the most popular machine-learning library. With strong distributed support, improved performance, and wider platform coverage (and more added through the year), it has seen widespread adoption across the industry.
Rajat Monga offers an overview of TensorFlow progress and adoption in 2016 before looking ahead to the areas of importance in the future—performance (speed, scale, and power—important components for the success of machine learning), usability (higher-level libraries and packages to make machine learning easier for more people to use), and ubiquity (machine-learning models that can run everywhere, from the cloud to the edge)—and the efforts TensorFlow is making in those areas.
Rajat Monga leads TensorFlow, an open source machine-learning library and the center of Google’s efforts at scaling up deep learning. He is one of the founding members of the Google Brain team and is interested in pushing machine-learning research forward toward general AI. Previously, Rajat was the chief architect and director of engineering at Attributor, where he led the labs and operations and built out the engineering team. A veteran developer, Rajat has worked at eBay, Infosys, and a number of startups.
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