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Machine Learning for JavaScript Developers TensorFlow.js

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Machine Learning (ML) is a branch of Artificial Intelligence(AI) that gives machines capabilities to learn and improve without explicit programming or human interference, it uses data to learn itself. TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. In simple terms, TensorFlow is a machine learning library made by Google used to design, build and train machine learning models. Google introduced TensorFlow in 2015 and was used with Python, though it has APIs in Java, C and Go.


TensorFlow.js puts machine learning in the browser

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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.


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

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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.


Machine Learning in JavaScript with TensorFlow.js

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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.


Introducing TensorFlow.js: Machine Learning in Javascript

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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. 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. In this post, we'll give you a quick overview of TensorFlow.js, and getting started resources you can use to try it out. Running machine learning programs entirely client-side in the browser unlocks new opportunities, like interactive ML!