JavaScript is the most popular language among web developers in the world. There are many reasons for this popularity. One of the most important reasons for its popularity is the numerous and powerful libraries of this language. Of course, maybe shortly JavaScript will become the popular language of engineers and experts in Machine Learning, Deep Learning, Natural Language Processing, and even Data Science.


Because JavaScript, in addition to having popular libraries in web development such as React, jQuery, Vue.js, etc., also has many other powerful libraries for ML, NLP, deep learning, and data science. The number of these libraries is large and each of them has special facilities and capabilities.

In this context, 7 JS libraries for deep learning, natural language processing, and data visualization that every programmer and developer should be familiar with in 2023 are introduced.

7 machine learning libraries in JavaScript (2023)

You may have heard that Python programming language libraries are used for artificial intelligence and machine learning. Yes, it’s quite true that Python and its powerful libraries are usually the first choice for experts in machine learning, natural language processing, and data visualization. Python and its specialized libraries played a major role in the development of artificial intelligence and its sub-branches.

But you should not think that Python is unrivaled in this field. There are other languages (such as the R programming language for data science) that professionals in these fields can use in one or some of their projects. JavaScript is a competitor that, like Python itself, has enough power and capabilities to be used in various projects and areas of artificial intelligence.

In this content, 2 libraries for deep learning (which is a type of machine learning based on artificial neural networks), 2 libraries for natural language processing, and 3 libraries for data visualization are introduced.

1. Synaptic

The goal of deep learning is for the machine to mimic the cognitive functions of the human brain. This imitation is made possible by artificial neural networks. Deep learning algorithms are composed of neural networks. Algorithms are trained with data to learn to act like the human brain and perform tasks.

Synaptic.js is an open-source library developed by MIT. This library has a large number of pre-made neural networks. The programmer and developer can easily insert any training or other data they want in these ready-made networks and modify each network according to their needs. Node.js and browser support neural networks of this library.

The advantage of using this library is that you can add networks to other networks and also as independent functions to JSON. JavaScript Object Notation (JSON) is a very important and practical technology used for asynchronous programming and asynchronous transfer of data on the web.

The browser executes the code line by line. This means that the execution of some codes and functions is suspended until the execution of the previous codes. In JavaScript, because Async is possible, the programmer can place the execution of a certain code or function earlier than the rest of the code. It means that the codes are not executed line by line.

Asynchronous code execution is important because it increases page loading speed and the user no longer needs to wait for the entire page to load to do a specific task.

2. ConvNetJS

convnetjs is a fully specialized library that allows developers and programmers to train neural networks in the browser without the need for additional software or programs. What this library does is train deep learning models involving neural networks in the browser.

ConvNetJS was developed by a PhD student at Stanford and its code is publicly available on GitHub. Professionals and experts welcomed this library and now it is supported by a network of experts. In addition to neural network training, ConvnetJS has options for solving classification and regression problems, image-focused convolutional network training, and a reinforcement learning module, which is still in the experimental stages.

3. Nlp.js

This library is undoubtedly one of the most useful and powerful libraries for natural language processing (NLP) professionals. In natural language processing, the expert’s goal is to design and train a model that can interact with humans in natural language. Artificial intelligence chatbots (such as GPT Chat and Google Bard) are built with natural language processing.

Nlp.js is designed for node.js (a JavaScript runtime environment that allows developers to use JS for server-side programming) and supports 40 languages.

Some of the features of this library are:

  • guessing the language of a phrase,
  • sentiment analysis of different expressions written in the same language,
  • Classify the purpose of each sentence.
  • Generating natural language to answer questions or input texts.

4. Compromise

compromise. cool is the second specialized JavaScript library for natural language processing. The main focus of this library is on identifying words and phrases, analyzing the text, and then interpreting the text.

5. D3.js

The final 3 libraries are JavaScript libraries for data science and data visualization. D3.js is a free and open-source library for data visualization using the JavaScript language, developed by Mike Bostock in 2011. Using this library, the web developer and programmer can create dynamic and animated visualizations to display on the website. D3.js is fully compatible with React and Vue.js frameworks and leaves the programmer’s hand completely free to get exactly the image and diagram he wants.

6. Chart.js

This open-source library is simpler than the previous one and has fewer but more useful features for website designers. Chart.js allows the developer to display data with 8 of the most common charts (bar, pie, bubble, line, histogram, scatter, error, and polar chart).

In addition to these 8 charts, the programmer can create composite charts or use the numerous plugins of this library or take advantage of the charts defined by other programmers and experts on GitHub. Also, the developer can animate any part of the chart using the animation features in Chart.js. A very important feature of charts made with this library is their responsiveness. That is, the size of the chart corresponds to the size (dimensions) of the device that opens the browser.

Finally, it should be added that Chart.js is fully compatible with the most important and famous JavaScript frameworks (React, Vue, Angular).

7. Sigma.js

Look at the image below. You can see the power, features, and differentiation of the sigma.js library. This is a library for depicting graphs, graphs that represent large amounts of data. Also, the possibilities of this library for interactive, coloring and personalization of graphs are many.


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