TensorFlow.js: Machine Learning for the Web and Beyond

01/16/2019

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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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TensorFlow.js: Machine Learning for the Web and Beyond

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TensorFlow.js: Machine Learning for the Web and Beyond

Related Research

TensorFlow RiemOpt: a library for optimization on Riemannian manifolds

TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning

TensorX: Extensible API for Neural Network Model Design and Deployment

tf-Darshan: Understanding Fine-grained I/O Performance in Machine Learning Workloads

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Private Machine Learning in TensorFlow using Secure Computation