acd593d2db87a799a8d3da5a860c028e@2019@MLSYS

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#1 TensorFlow.js: Machine Learning For The Web and Beyond [PDF] [Copy] [Kimi] [REL]

Authors: Daniel Smilkov ; Nikhil Thorat ; Yannick Assogba ; Charles Nicholson ; Nick Kreeger ; Ping Yu ; Shanqing Cai ; Eric Nielsen ; David Soegel ; Stan Bileschi ; Michael Terry ; Ann Yuan ; Kangyi Zhang ; Sandeep Gupta ; Sarah Sirajuddin ; D Sculley ; Rajat Monga ; Greg Corrado ; Fernanda Viegas ; Martin M Wattenberg

TensorFlow.js is a library for building and executing machine learning algorithms in JavaScript. TensorFlow.js models run in a web browser and in the Node.js environment. The library is part of the TensorFlow ecosystem, providing a set of APIs that are compatible with those in Python, allowing models to be ported between the Python and JavaScript ecosystems. TensorFlow.js has empowered a new set of developers from the extensive JavaScript community to build and deploy machine learning models and enabled new classes of on-device computation. This paper describes the design, API, and implementation of TensorFlow.js, and highlights some of the impactful use cases.