Machine Learning in JavaScript

Machine learning libraries are growing faster and more available with each passing year, showing no signs of breaking down. While traditionally Python has been the go-to language for machine learning, now-a-days neural networks can run in any language, including JavaScript!

The web system has made a lot of development in recent times and although JavaScript and Node.js are still fewer performers than Python and Java, they are now dominant to handle many machine learning hitches.

Most of the JavaScript machine learning libraries are fairly new and still in improvement, but they do exist and are ready for the users to try them. In this article, we will look at some of these libraries, as well as a number of cool AI web app instances to get you started.


The brain is a library that makes the user to easily create neural networks and then train them depending on input/output data. As training takes up a lot of resources, it is better to run the library in a Node.js location, though a CDN browser version can also be loaded directly onto a web page. There is a small demo on their website that can be trained to identify color contrast.


It is the most vigorously maintained project on the list, Synaptic is a Node.js and browser library that is architecture-agnostic, allowing the developers to build any type of neural network they want. It has some narrow built-in architectures, making it likely to test and relate different machine learning algorithms. It also features a well-published introduction to neural networks, a number of practical demos, and many other great tutorials illustrating how machine learning works.


FlappyLearning is a JavaScript project that is of hardly few lines of un-minified code copes to build a machine learning library and implement it in a fun demo that learns to play Flappy Bird like a virtuoso. The AI method used in this library is called Neuroevolution and applies algorithms motivated by nervous systems found in nature, dynamically learning from each iteration’s success or failures. The demonstration is super easy to run – just open index.html in the browser.


Though it is no longer actively maintained, ConvNetJS is one of the most progressive deep learning libraries for JavaScript. It works directly in the browser, supports several learning techniques, and is rather low-level, making it appropriate for people with better experience in neural networks.


Framework for building AI systems based on reinforcement learning. Miserably, the open-source project does not have a right documentation but one of the demos, a self-driving car experiment, has a great description of the different parts that make up a neural network. The library is in pure JavaScript and made using modern tools such as web pack and babel.


Although the JavaScript machine learning ecosystem is not completely developed yet, we suggest using the resources on this list to make your first steps in ML and get a feel for the core techniques. As the experimentations in this article show, there are loads of exciting stuff you can make by using only the browser and some familiar JavaScript code.

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