The tremendous growth of the machine learning field has been driven by the availability of open source tools that allow developers to build applications easily. (For example, AndreyBu[1], who is from Germany and has more than five years of experience in machine learning, has been utilizing various open source frameworks to build captivating machine learning projects.)
Although the Python programming language powers most of the machine learning frameworks, JavaScript hasn’t been left behind. JavaScript developers have been using various frameworks for training and deploying machine learning models in the browser.
Here are the five trending open source machine learning frameworks in JavaScript.
1. TensorFlow.js
TensorFlow.js[2] is an open source library that allows you to run machine learning programs completely in the browser. It is the successor of Deeplearn.js, which is no longer supported. TensorFlow.js improves on the functionalities of Deeplearn.js and empowers you to make the most of the browser for a deeper machine learning experience.
With the library, you can use versatile and intuitive APIs to define, train, and deploy models from scratch right in the browser. Furthermore, it automatically offers support for WebGL and Node.js.If you have pre-existing trained models you want to import to the browser, TensorFlow.js will allow you do that. You can also retrain existing models without leaving the browser.
The machine learning tools[3] library is a compilation of resourceful open source tools for supporting widespread machine learning functionalities in the browser. The tools provide support for several machine learning algorithms, including unsupervised learning, supervised learning, data processing, artificial neural networks (ANN), math, and regression.
If you are coming from a Python background and looking for something similar to Scikit-learn for JavaScript in-browser machine learning, this suite of tools could have