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arxiv: 1901.05350 · v2 · submitted 2019-01-16 · 💻 cs.LG

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TensorFlow.js: Machine Learning for the Web and Beyond

Daniel Smilkov , Nikhil Thorat , Yannick Assogba , Ann Yuan , Nick Kreeger , Ping Yu , Kangyi Zhang , Shanqing Cai
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Eric Nielsen David Soergel Stan Bileschi Michael Terry Charles Nicholson Sandeep N. Gupta Sarah Sirajuddin D. Sculley Rajat Monga Greg Corrado Fernanda B. Vi\'egas Martin Wattenberg
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classification 💻 cs.LG
keywords tensorflowjavascriptlearningmachinemodelslibrarypythonalgorithms
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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.

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