6 Top Machine Learning Libraries for Javascript in 2019

by Radek Fabisiak

Intro to Machine learning libraries for Javascript

Usually, people apply machine learning (ML) methods and algorithms using one of two programming languages: Python or R. Books, courses, and tutorials about Machine Learning most often use one of these languages as well (or both).

Python is a general-purpose programming language used not only for Machine Learning but for also for scientific computing, back-end Web development, desktop applications, etc. R is created primarily for statisticians. However, they have at least two common characteristics:

Java is also used for Machine Learning, but usually by professional programmers.

During the last few years, JavaScript gained on popularity, and some very interesting Machine Learning libraries appeared enabling implementation of ML methods in browsers or on Node.js. Surprisingly, many of such libraries implement a lot of code in JavaScript itself.

This article presents several ML open-source libraries for JavaScript:


ml.js is a comprehensive, general-purpose JavaScript ML library for browsers and Node.js. It offers the routines for:

Supported supervised learning methods are:

Besides, ml.js offers several unsupervised learning methods:

License: MIT.


TensorFlow is one of the most popular Machine Learning libraries. It focuses on various types and structures of artificial neural networks, including deep networks, as well as the components of the networks. TensorFlow is created by Google Brain Team and written in C++ and Python. However, it can be used with several languages, including JavaScript.

TensorFlow is a very comprehensive library that still enables building and training models easily. It supports a huge variety of network layers, activation functions, optimizers, and other components. It has good performance and offers GPU support.

TensorFlow.js is a JavaScript ML library for use in browsers or on Node.js. It supports WebGL.

License: Apache 2.0.


brain.js is a library written in JavaScript-focused on training and applying feedforward and recurrent neural networks. It also offers additional utilities, such as math routines necessary for neural networks.

It provides advanced options like:

brain.js saves and loads models to/from JSON files.

License: MIT.


ConvNetJS is another library for neural networks and deep learning. It enables training neural networks in browsers. In addition to classification and regression problems, it has the reinforcement learning module (using Q-learning) that is still experimental. ConvNetJS provides support for convolutional neural networks that excel in image recognition.

In ConvNetJS, neural networks are lists of layers. It provides the following layers:

It supports several important activation functions like:

as well as the optimizers such as:

License: MIT.


WebDNN is a library focused on deep neural networks, including recurrent neural networks with LSTM architecture. It is written in TypeScript and Python and offers JavaScript and Python APIs.

It also provides the possibility of GPU execution in browsers.

A very convenient feature of WebDNN is the possibility to convert and use the models pretrained with PyTorch, TensorFlow, Keras, Caffemodel or Chainer.

License: MIT.


natural is a JavaScript library for natural language processing used with Node.js.

It supports:

License: MIT.


Both JavaScript and machine learning are gaining much attention and popularity during the last several years. Although initially created to enable dynamic behavior of Web pages, JavaScript becomes one of the languages of choice to implement and apply machine learning methods, especially in browsers or servers (Node.js).

This article provided the initial information on the availability of machine learning libraries for JavaScript.

Have a lot of fun exploring them!

Radek Fabisiak

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