Machine Learning is one of the most trending technologies today with businesses, big and small, coming forward to invest in it; primarily to prevent/avoid risks, make informed decisions and grow faster. When leveraging ML techniques and algorithms, enterprises generally use either Python or R. Furthermore, most ML courses and tutorials also use one of these two programming languages.

Python has been around for a while and is being used for quite a lot of purposes other than Machine Learning, including backend development, desktop app development, advanced computing etc. R, on the other hand, is primarily used by statisticians and data miners. Both languages also come with all-inclusive ML libraries.

There is one other language that’s used for machine learning albeit by professional programmers; experts in their craft – Java. JavaScript has always been popular but recently gained a lot of momentum when some very interesting machine learning libraries popped up. With this, programmers can now implement ML methods in browsers or on Node.js.

That said, this blog covers a few open source machine learning libraries for JavaScript that every ML developer should check out.

TensorFlow.js

TensorFlow is likely the most popular machine learning library in our list. It’s designed to focus on various types of artificial neural networks and network components. The library is the brainchild of Google Brain Team, and can be used with several languages including JavaScript.

With TensorFlow, ML models can be built and trained easily as the library supports a number of activation functions, network layers, optimizers and various other components. It also features GPU support and is praised for its performance.

natural

You might have guessed from the name by now that natural has something to do with natural language processing which is closely associated with machine learning. natural is a library for NLP with Node.js.

Licensed under MIT, the open source library supports Tokenization, strings matching, sentiment analysis, phonetic matching and more.

WebDNN

A library designed to be used on deep neural networks, WebDNN is written in TypeScript and Python. It offers Python and JavaScript APIs, and also facilitates GPU execution on browsers. WebDNN is primarily used on recurrent neural networks with LSTM architecture. With this library, machine learning architects may convert and use pre-trained models with TensorFlow, Caffemodel, PyTorch etc.

brain.js

Written in JavaScript itself, brain.js like WebDNN works with recurrent neural networks and is primarily focused on training and applying feedforward. It features math routines that can be useful for neural networks and offers a variety of options including GPU-driven network training, asynchronous network training on multiple networks simultaneously, loading models to and from JSON files, cross validation etc.

ml.js

ml.js is a general purpose JavaScript ML library for browsers and Node.js. It features routines for both supervised and unsupervised learning, cross validation, array manipulation and optimization, and bit operations.

Feedforward neural networks, vector machines support, Naïve Bayes, decision trees etc. are just a few of the supervised learning methods supported by the popular open source library.

Unsupervised learning methods include principal component analysis, cluster analysis, self-organizing maps etc.

Conclusion

The last several years saw the increasing application of JavaScript for machine learning enablement. Many experienced programmers would vouch for the fact that JavaScript is indeed a great choice for applying machine learning methods, especially on browsers or servers (Node.js).

If you are interested in exploring the prospects of JavaScript-powered machine learning applications for your enterprise, feel free to have a chat with the AI/ML experts at AOT.

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