Machine Learning is one of the fastest growing technologies of the modern era. Use and popularity of programming languages, such as R, Python, Go, Lisp, Java, and Prolog are growing every day due to an increasing need for machine learning and automation. Popular programming languages such as Java, C#, and C++ can also be used to build AI and machine learning algorithms applications with the support of some libraries. But what about JavaScript?
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JavaScript and web applications are growing. Today, JavaScript is one of the most popular web development scripting languages in the world. Now, thanks to TensorFlow.js, JavaScript can now be used to build and run machine learning models. TensorFlow.js is a JavaScript library for training and deploying machine learning models in the browser and on Node.js.
TensorFlow.js has three main use cases:
- TensorFlow.js provides a set of flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.
- TensorFlow.js uses model converters to run pre-existing models right in the browser or under Node.js.
- TensorFlow.js lets you retain pre-existing ML models using sensor data connected to the browser, or other client-side data.
To get started with TensorFlow.js and learn what it is, watch the following video.
Here is a detailed video on how to get started with ML using JavaScript and TensorFlow.js.