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Best JavaScript Libraries for AI and Machine Learning

There are countless JavaScript Libraries for AI and Machine Learning available on npm and Github. These libraries certainly helpful in artificial intelligence and machine learning.

Only a few of them are coming from experienced developers. They know the details of these technologies very well.

Here, we have a list of the top JavaScript libraries. That make AI and ML more accessible to web developers like you.

Each library has its own unique functionality. You can also use as many or as few as you need for your project.


This library offers an interface to Google’s TensorFlow, an open-source machine learning library designed to work with numerical data.

Because it is a web-based library, so you don’t need to install anything on your computer in order to use it.

However, you will need some data that you want to pass through a neural network. That make predictions obviously easy and accurate about other inputs of similar characteristics.

TensorFlow.js can make better computations over large dataset. Which allows computers to make sense of their environment in new ways.

It’s so easy that there are already many experiments available in the market like The Great AI Awakening or Voicebox.


Mind, a JavaScript library for neural networking that works with browsers and Node.js to improve predictions, hence it is incredibly adaptable.

One of Mind’s features is using a matrix implementation to analyze training data while letting programmers alter the network architecture.

It gives you access to a wide variety of machine learning algorithms. It’s source-code is in pure JavaScript, fast, flexible, easy to use, open-source, and has advance features like seamless distributed training support.

This library makes it very easy to get start on your projects. It is faster pluggable and simpler to download and submit plugins than other libraries.

Another benefit of Mind is the simplicity with which pre-trained networks may be configured.

There are plenty more great libraries out there. The ones mentioned here are some of the most popular and have high development activity.


ConvNetJs is a JavaScript library to help web developers train deep neural networks in browsers. Hence it is simple to use and quick without a GPU.

Andrej Karpathy, a Stanford Ph.D. student, developed this project in an effort to make neural networks more approachable.

The library can use WebGL shaders. It works with popular frameworks like Keras, TensorFlow.js, Google’s Protobuf Compiler, or Caffe2 from Facebook among others.


An artificial neural network library for JavaScript. NeatJS is an artificial neural network library in JavaScript. Basically, it focuses on neat code clarity, useability, performance, and Node.js support.

It uses Math.js for its mathematical functions to provide flexible numerical operations between tensors or with its built-in data types. Which allows you to create complex structures from scratch or from existing ones using functional transformations like maps, filters, etc.

The library also comes with a GUI that allows you to develop your own networks in an intuitive way. It has a fairly rich set of features. It includes a native BatchNorm layer and Caffe-ConvNet model conversion tool among others.


deeplearn.js is a free and open-source hardware-accelerated JavaScript library for machine learning.

A general-purpose library for machine intelligence based on neural networks. It’s source code is entirely in JavaScript. It runs on Node.js, browsers, or Web Workers, with support for GPU acceleration with WebGL 2 or WebAssembly.

deeplearn.js has two APIs. One for immediate execution (similar to NumPy) and one for deferred execution (similar to the TensorFlow API).

Deeplearn.js is a product of the Google Brain PAIR team. Its purpose is to create powerful interactive machine learning tools for the browser.

This library is useful for a variety of purposes ranging from education to model understanding to art projects.

It is already in production by Yahoo! Labs and Google DeepMind, among others. It is not a research prototype but a robust library that is ready to solve real-world problems.


Library of Neural Network models that is using a simple syntax. Models include neural networks, support vector machines, deep belief networks, recurrent neural networks, and more.

You can combine models by stacking them on top of each other to create more complex models. The brain.js also supports online learning for most of its models.

This allows them to adjust their parameters as new data is provided. It makes it useful in supervised learning situations where data is not static.

The library includes built-in training functionality for many types of models. This allows code to run both synchronously or asynchronously making it ideal for large datasets or tight latency requirements.


Synaptic is a free and open-source library, anyone can contribute to it or use it. This is an MIT JavaScript neural network library that is useful with Node.js or the browser.

Because of its architecture-free algorithm and pre-manufactured structure, this library can build and train any first-order or second-order neural network architecture.

It can import and export networks to JSON in order to connect to other networks or even gate connections.

Hopfield networks, state machines, multilayer perceptrons, long short-term memory networks, and other interesting architectures are the best examples of it.

This library can train any neural network using tests such as an Embedded Reber Grammar test, solving an XOR, etc.

It also aids in the comparison of the performance of various neural network architectures.


We discussed some best JavaScript libraries that you can use when working with machine learning.

Although JavaScript is unfamiliar with subjects such as deep learning and machine learning. It may be the most popular language among ML developers in the coming years.

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