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Machine Learning in JavaScript with TensorFlow.js


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.

Machine Learning for JavaScript Developers TensorFlow.js


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 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.

Machine Learning In Node.js With TensorFlow.js - James Thomas


TensorFlow.js is a new version of the popular open-source library which brings deep learning to JavaScript. Developers can now define, train, and run machine learning models using the high-level library API. Pre-trained models mean developers can now easily perform complex tasks like visual recognition, generating music or detecting human poses with just a few lines of JavaScript. Having started as a front-end library for web browsers, recent updates added experimental support for Node.js. This allows TensorFlow.js to be used in backend JavaScript applications without having to use Python.

Eliminating Performance Bottlenecks on Web-Based AI - InformationWeek


Web developers are beginning to build artificial intelligence into their apps. More of them are experimenting with new frameworks that use JavaScript to compose machine learning (ML), deep learning (DL), and other AI functions. The most significant JavaScript toolkit for building Web-embedded AI is TensorFlow.js, which Google announced at its developer conference in late March. It enables interactive modeling, training, execution, and visualization of ML, DL, and other AI models. It encompasses an ecosystem of JavaScript tools for ML, including TensorFlow.js