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TensorFlow.js: Machine Learning for the Web and Beyond

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arxiv 1901.05350 v2 pith:XSOXYBLY submitted 2019-01-16 cs.LG

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
This is my paper
classification cs.LG
keywords tensorflowjavascriptlearningmachinemodelslibrarypythonalgorithms
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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