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Machine Learning in JavaScript with TensorFlow.js

#artificialintelligence

TensorFlow.js is a library for Machine Learning in JavaScript. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js. If you're a Javascript developer who's new to ML, TensorFlow.js is a great way to begin learning. Or, if you're a ML developer who's new to Javascript, read on to learn more about new opportunities for in-browser ML. We're excited to introduce TensorFlow.js, an open-source library you can use to define, train, and run machine learning models entirely in the browser, using Javascript and a high-level layers API.


TensorFlow.js puts machine learning in the browser

#artificialintelligence

Google's TensorFlow open source machine learning library has been extended to JavaScript with Tensorflow.js, a JavaScript library for deploying machine learning models in the browser. A WebGL-accelerated library, Tensorflow.js also works with the Node.js With machine learning directly in the browser, there is no need for drivers; developers can just run code. The project, which features an ecosystem of JavaScript tools, evolved from the Deeplearn.js APIs can be used to build models using the low-level JavaScript linear algebra library or the higher-level layers API.


tensorflow/tfjs

@machinelearnbot

TensorFlow.js is an open-source hardware-accelerated JavaScript library for training and deploying machine learning models. Develop ML in the Browser Use flexible and intuitive APIs to build models from scratch using the low-level JavaScript linear algebra library or the high-level layers API. Run Existing models Use TensorFlow.js model converters to run pre-existing TensorFlow models right in the browser. Retrain Existing models Retrain pre-existing ML models using sensor data connected to the browser, or other client-side data. Alternatively you can use a script tag.


TensorFlow.js: Machine learning for the web and beyond

#artificialintelligence

If machine learning and ML models are to pervade all of our applications and systems, then they'd better go to where the applications are rather than the other way round. Increasingly, that means JavaScript – both in the browser and on the server. TensorFlow.js brings TensorFlow and Keras to the the JavaScript ecosystem, supporting both Node.js and browser-based applications. As well as programmer accessibility and ease of integration, running on-device means that in many cases user data never has to leave the device. On-device computation has a number of benefits, including data privacy, accessibility, and low-latency interactive applications.


Blog - ML Conference TensorFlow.js: What is possible with Machine learning in the browser?

#artificialintelligence

TensorFlow.js is a JavaScript library that runs in a browser as well as with Node.js on a server. However, in this article our scope of interest is only for the application in the browser. Keras code is often only distinguishable from TensorFlow.js code at second glance. Most differences are due to the different language constructs of Python and JavaScript for configuration parameters. TensorFlow.js allows you to build machine learning projects from zero.