TensorFlow.js: Machine Learning for the Web and Beyond

2019/01/16 by Daniel Smilkov, Smilkov, Daniel, Nikhil Thorat +37 · 1 voice · 1 citation
#cs.LG

paper · pdf · doi:10.48550/arxiv.1901.05350

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.

Cited by

• Smart Contract Intent Detection with Pre-trained Programming Language Model

Discussions

• TensorFlow.js: Machine Learning for the Web and Beyond [hn, 192 points, 51 comments]

Related

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• TensorFlow: A system for large-scale machine learning
• Should I use TensorFlow
• Moving Deep Learning into Web Browser: How Far Can We Go?
• TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning