Top JavaScript-Based Machine Learning Frameworks

JavaScript Machine Learning Frameworks

While Python and C++ programming languages have become a popular choice when it comes to machine learning framework, JavaScript is not too far behind. Looking around, one may find that JavaScript frameworks have also been implemented in AI. In fact, as per the GitHub review of best machine learning technologies, JavaScript occupies the third position after Python and C++, while R falls in the eighth place. These JavaScript frameworks are boosting business growth with artificial intelligence and machine learning. In this article, in no particular order, we list top JavaScript-based machine learning frameworks.


Brain.js is an open-source, JavaScript-based framework that simplifies the process of defining, training and running neural networks. It can be used with Node.js or at the client-side browser for training machine learning algorithms. This framework is particularly useful for individuals who are just starting out in machine learning and apprehensive to math-heavy technicalities and jargons. 

Brain.js supports several networks such as feed-forward networks, recurrent neural networks, Ellman networks, and long short-term memory networks.

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TensorFlow.js is an end-to-end open-source framework maintained by Google. TensorFlow forms the foundation of network software such as DeepDream that can capture, detect, and classify images. It consists of several tools, libraries and other resources for application development on deep neural networks. While the original TensorFlow was built on a Python interface with a highly optimised C++ core, in 2018, Google released TensorFlow.js, a JavaScript machine learning framework. It allows programmers to import existing machine learning models, re-train them or build new ones and deploy with Node.js or on the client-side.


Like TensorFlow, Keras is originally written in Python and is the second most popular deep learning framework after TensorFlow, with over 250,000 individual users. Several tech heavyweights such as Yelp, Uber, and Netflix have been utilising Keras models. The javascript version Keras.js helps in running Keras models in the client’s browser with GPU support provided by WebGL. These models can also run on Node.js but only in CPU mode.


Neuro.js is a JavaScript framework for machine learning, in particular, deep learning. It contains demos to visualise reinforcement learning and neural network-based capabilities. It also provides support for the implementation of full-stack neural-network-based machine learning framework, deep-q-networks, actor-critic models, and binary import and export of network configuration. 


ML5.js is one of the most popular and widely used machine learning frameworks running on TensorFlow.js. It can handle GPU accelerated mathematical operations and provide memory management for machine learning algorithms. ML5.js gives good performance by using TensorFlow.js internally as it provides an intuitive interface to developers. Users get access in the browser to pre-trained models for detecting human poses, image styling, pitch detection, and even music composition.


Designed in Japan, WebDNN helps in the quick execution of deep neural networks in web browsers. It is highly efficient and delivers superior performance. WebDNN optimises models and compresses data. Like Keras.js, WebDNN utilises WebGPU, a next-generation JavaScript API for rendering 3D graphics, for running models on GPU. Further, with the help of WebAssembly, the framework speeds up CPU execution. WebAssmbly compiles the code in high-level programming languages into smaller lightweight modules.


DeepForge is not just a framework but also a user-friendly development environment for deep learning. With DeepForge, users can design neural networks with simple graphical interfaces. It also supports training models on remote machines and has a built-in control. DeepForge is based on Node.js and MongoDB, and its installation process is very similar to most web devs.

Wrapping Up

JavaScript is yet far away from replacing Python as the preferred language for machine learning. However, it proves to be a good alternative. JavaScript machine learning is capable of handling a large set of data, provides good performance, and has a large number of useful libraries. Hence, the above listicle can prove to be a useful resource for JavaScript developers starting their stint at machine learning or machine learning experts who are looking beyond the usual Python language.

Shraddha Goled
I am a technology journalist with AIM. I write stories focused on the AI landscape in India and around the world with a special interest in analysing its long term impact on individuals and societies. Reach out to me at

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