10 Mind Blowing JavaScript libraries Of 2022 (I mean it Javascript Noob)
Tags: javascript, machinelearning, python, datascience
Published: 2022-06-30 02:40:07

**JavaScript** is very significant because it is the web's primary programming language.

In contrast to R and Python, it has, however, primarily been utilised as a scripting language in web construction with little association with machine learning or data science.

That's because R and Python have a wide range of supporting libraries, community members, and infrastructure, making them particularly well suited to Data Science or Machine Learning.

The popularity of JavaScript, however, has skyrocketed in recent years to the point where more and more people are obsessing over it.

The top 10 JavaScript libraries that are currently quite popular are covered in this post as a result.


![javascript libraries](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/70r5bjaauahumzxrixj0.png)

## (1) [Brain.js](https://brain.js.org/#/)

A Javascript library for machine learning, namely neural networks, is called Brain.js. It may go back to pure JavaScript when GPU is not available, which makes it pretty quick because it leverages GPU for computations.


![Brain.js](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/nf6429fnzs2bqef4hp0w.png)

The best part about using this library is that you don't need to be an expert in neural networks to use the implementations for different kinds of neural networks that Brain.js offers. These models can also be imported as a function or in JSON format, and you can incorporate them into your website.

Wonderful isn't it.

## (2) [TensorFlow.js](https://www.tensorflow.org/js)

TensorFlow.js is a machine learning toolkit written in Javascript that offers a wide range of tools, libraries, and resources. You can either convert your Python models or use the official TensorFlow models that are already available.

![TensorFlow.js](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/4cvuawahejwgbxzaahzq.png)

Additionally, you can retrain pre-built machine learning models using your own data. Regardless of the language you select, you may deploy the machine learning models everywhere, including the cloud, the browser, on-premises, or on the device.

TensorFlow, however, comes in many other flavours, like TensorFlow Lite for mobile devices, TensorFlow Extended for the complete experience, TensorFlow Rust for Rust bindings, etc.

## (3) [Synaptic](https://caza.la/synaptic/#/)

For use with node.js and the browser, the JavaScript neural network library Synaptic was developed. As separate functions, the networks can also be imported or exported to JSON. They may have gate connections or connections to other networks.


![Synaptic js](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/bpr2548ow45e01cb5mqs.png)

Additionally, the library includes numerous practical built-in structures, including liquid state machines, multilayer long-short term memory (LSTM) networks, multilayer perceptrons, Hopfield networks, etc., along with trainers that may use any training set and any network type. Additionally, Synaptic is an open-source library from MIT that anybody can use or contribute to.

## (4) [ConvNetJS](https://cs.stanford.edu/people/karpathy/convnetjs/)

ConvNetJS is a javascript library designed primarily for neural network-based deep learning models. The main benefit of this library is that it can be used exclusively in browsers without the need for other software like GPUs, compilers, etc.

![ConvNetJS](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/y7pgn3wygmcwevuljvwf.png)

ConvNetJS offers solutions for neural networks, classification and regression issues, image-focused convolutional networks, and an experimental reinforcement learning module.

## (5) [ml5.js](https://ml5js.org/)

With no other external dependencies, ml5.js is a Javascript machine learning library built on top of TensorFlow. It gives users access to a number of machine learning pre-trained algorithms that are used for producing music, detecting human poses, detecting pitch, styling images with other images, generating text, discovering word associations in the English language, etc.

![ml5.js](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/70wl6yo4k4cv8kictmeu.png)

The main goal of ml5.js is to help people better understand machine learning and all of its intricacies, such as responsible data collection, ethical computing, etc.

## (6) [nlp.js](https://www.npmjs.com/package/node-nlp)

A Javascript-based natural language tool for Node.js is provided by nlp.js. It may infer the language of a sentence or get the stemmers and tokenizers for several languages, among many other functions. Additionally, nlp.js has the ability to analyse sentiment for various phrases written in a specific language.

![nlp.js](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/nl0kpnteuafn3uinidgu.png)

The Natural Language Processing Classifier and the Natural Language Generation Manager, respectively, can be used to categorise the intent of any sentence and subsequently produce an answer for the sentence depending on the intent. 40 languages are supported by nlp.js natively, and 104 more can be supported with BERT integration.

Fascinating isn't it.

## (7) [Compromise](http://compromise.cool/)

A JavaScript library called Compromise is dedicated to natural language processing, making it simpler to comprehend and pre-parse text so that judgments may be made based on it. Many words can be compressed through compromise and then expanded at runtime to produce the assumptions.

14,000 words that are compressed into a 40kb file can cover approximately 99.99 percent of the whole English language's lexicon. As a result, there is a significant compromise in how quickly words are understood and scanned with very little latency.

## (8) [D3.js](https://d3js.org/)

A JavaScript framework called D3 or Data-driven documents can be used to alter data using HTML, CSS, and SVG to create unique data visualisations. Document object models can be used by D3 to combine documents, which can subsequently be transformed based on the specifications.

Along with typical charts like scatter plots, line charts, bar charts, and pie charts, D3 also provides a variety of chart styles for data analysis, including histograms, box plots, treemaps for hierarchies, and chard graphs for networks. D3 offers a variety of animation choices, including a moving treemap, zoomable bar charts, icicles, bar chart competitions, etc.

## (9) [Chart.js](https://www.chartjs.org/)

Bar charts, pie charts, histograms, scatterplots, error charts, and other common charts are all included in the open-source Chart.js javascript charting library's eight main chart types.

These charts can all be merged to create mixed charts that can be animated and customised. Additionally, Chart.js renders effortlessly across all web browsers and scales the chart to fit the window size. If a time axis is required, all the charts in this library can easily be used using the moment.js library.

## (10) Sigma.js

Sigma.js is a programme that is specifically designed for drawing graphs, which are a crucial component of data visualisation. It has built-in tools that make posting graphs on websites and visualising them easier. Sigma.js offers options for mouse and touch capabilities, custom rendering, improved accessibility, and more. It also supports Canvas and WebGL. To increase the amount of engagement with the graphs, you may also alter the data, move your camera, listen to events, and alter the rendering.

The top 10 JavaScript libraries for various aspects of machine learning and data science have been shown. Compared to Python or R, JavaScript is less well-known in these domains, but it is growing in popularity.

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