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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, Eric Nielsen, David Soergel, Stan Bileschi, Michael Terry, Charles Nicholson, Sandeep N. Gupta, Sarah Sirajuddin, D. Sculley, Rajat Monga, Greg Corrado, Fernanda B. Viegas, Martin Wattenberg
Google Brain
arXiv:1901.05350 [cs.LG], (16 Jan 2019)

@misc{smilkov2019tensorflowjs,

   title={TensorFlow.js: Machine Learning for the Web and Beyond},

   author={Daniel Smilkov and Nikhil Thorat and Yannick Assogba and Ann Yuan and Nick Kreeger and Ping Yu and Kangyi Zhang and Shanqing Cai and Eric Nielsen and David Soergel and Stan Bileschi and Michael Terry and Charles Nicholson and Sandeep N. Gupta and Sarah Sirajuddin and D. Sculley and Rajat Monga and Greg Corrado and Fernanda B. Viegas and Martin Wattenberg},

   year={2019},

   eprint={1901.05350},

   archivePrefix={arXiv},

   primaryClass={cs.LG}

}

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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