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, Eric Nielsen, David Soergel,, Stan Bileschi, Michael Terry, Charles Nicholson, Sandeep N. Gupta, Sarah, Sirajuddin, D. Sculley, Rajat Monga, Greg Corrado
TL;DR
TensorFlow.js is a JavaScript library that enables building and executing machine learning models directly in web browsers and Node.js, facilitating on-device AI and broadening developer access.
Contribution
This paper introduces TensorFlow.js, a library that bridges Python-based machine learning models to JavaScript, supporting web and on-device AI applications.
Findings
Models run efficiently in browsers and Node.js environments
Enables new on-device AI use cases
Broadens access for JavaScript developers
Abstract
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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Taxonomy
TopicsScientific Computing and Data Management · Time Series Analysis and Forecasting · Data Stream Mining Techniques