JavaScript is the programming language of the web, which makes it very important! However, in contrast to R and Python, it is primarily used as a scripting language in web development and has little to do with machine learning or data science. This is because R and Python are particularly well-suited for data science or machine learning, with a large number of support libraries, community members, and infrastructure. However, in recent years, the popularity of JavaScript has driven more and more people crazy! That’s why this article is going to talk about the 10 Javascript AI library collections that are very popular today.
A collection of Javascript machine learning libraries
These include a lot of great collections of Javascript machine learning libraries, different implementations of machine learning and data science that are now used, and what are the Javascript machine learning libraries？ Examples include nlp.js or compromise for natural language processing, D3.js or Chart.js for data visualization, and Brain.js, TensorFlow.js, and so on. Used for general machine learning. You can use Node.js in your browser and backend and use these libraries to implement all of these aspects of machine learning or data science in JavaScript. So, without further ado, let’s check out these libraries now.
Javascript AI Library Collection: Machine Learning
Brain.js
Brain.js is a JavaScript library for machine learning and neural networks. It’s very fast because it uses the GPU for computation and also has the ability to revert back to pure JavaScript when the GPU isn’t available. Brain.js provides various types of neural network implementations, and the best thing is that you don’t need to be very familiar with neural networks to use this library. You can also import these models as functions or in JSON format and integrate them into your website.
TensorFlow.js
Javascript Machine Learning Library Collection: TensorFlow.js is a machine learning library in javascript with a comprehensive and flexible variety of machine learning tools, libraries, and resources. You can run the official TensorFlow model that is already available, or you can convert your Python model. There are also pre-existing machine learning models that you can retrain with your own data. You can also deploy machine learning models anywhere, including the cloud, browser, on-premises, or device, regardless of the language you use. However, TensorFlow.js is only one version of TensorFlow, there are many other options available, such as TensorFlow Lite for mobile devices, TensorFlow Extended for full experiences, TensorFlow Rust for Rust bindings, etc.
Synaptic
Synaptic is a JavaScript neural network library created for node.js and browsers. Networks can also be imported or exported to JSON as standalone functions. They can be connected to other networks or even door connections. The library also has a number of useful built-in architectures, such as liquid machines, multilayer long short-term memory networks (LSTMs), multilayer perceptrons, Hopfield networks, etc., combined with trainers that can take any type of network and use any training set. Synaptic is also an open-source library for MIT, so anyone can contribute or use it for free.
ConvNetJS
What are the Javascript machine learning libraries? ConvNetJS is a javascript library designed to train deep learning models that include neural networks. One of the great advantages of this library is that it can be used entirely in the browser, with no special software requirements for GPUs, compilers, etc. ConvNetJS has neural networks, classification and regression problems, options for convolutional networks that focus on images, and reinforcement learning modules that are in the experimental phase.
ml5.js
Javascript Machine Learning Library Collection: ml5.js is a TensorFlow-based javascript machine learning library with no external dependencies. It allows access to a variety of machine learning pre-trained algorithms in the browser that are used to detect human poses, detect pitch, style images, generate text, find English word relationships, compose music, and more. ml5.js has a particular focus on giving people a deeper understanding of machine learning and its complexities, such as responsible data collection, ethical computing, and more.
Javascript AI Library Collection: Natural Language Processing
nlp.js
nlp.js provides a JavaScript-based natural language utility for NodeJS. It has many different features, such as guessing the language of a phrase or getting a stemmer and tokenizer for different languages. nlp.js is also capable of sentiment analysis of different phrases written in a particular language. You can also classify the intent of any sentence, and then use the Natural Language Processing Classifier and Natural Language Generation Manager to generate answers to the sentence based on intent, respectively. nlp.js natively supports 40 languages, while it also supports a further 104 languages with BERT integration.
Compromise
Compromise is a JavaScript library that focuses specifically on natural language processing in order to more easily interpret and pre-parse text to make decisions based on the text. Compromise can condense a lot of words and expand them at runtime, resulting in hypotheses. About 99.99% of English vocabulary can be processed by 14,000 words, which are compressed into a file size of only 40kb. This makes it possible to compromise very quickly when understanding and scanning words, with latency in low milliseconds.
Data Science & Visualization
D3.js
D3 or Data Driven Document is a JavaScript library that can be used to manipulate data using HTML, CSS, and SVG for custom data visualizations. D3 has the ability to combine documents with the Document object model and then transform documents as needed. D3 also has different chart types for data analysis, such as hierarchies such as box plots, histograms, tree charts, networks such as chard charts, and common charts such as scatter charts, line charts, bar charts, pie charts, etc. D3 also offers animation options like animated treemaps, zoomable bar charts, icicles, bar chart contests, and more.
Chart.js
What are the Javascript machine learning libraries? Chart.js is an open-source JavaScript chart library that offers 8 wide range of chart types, including all common charts like bar charts, pie charts, histograms, scatter charts, error charts, and more. All of these charts can be combined to generate hybrid charts, which are customizable and can also be animated. Chart.js can also be easily rendered on all web browsers and resize the chart according to the window size on the web browser. All the charts in the library can also be used with the moment.js library if a timeline is required.
Sigma.js
Javascript Machine Learning Library Collection: Graphs are a very important part of data visualization, sigma.js with a particular focus on graphing. It has built-in features to simplify graphical visualization and publish it on web pages. Sigma.js has options like Canvas and WebGL support as well as mouse and touch support, custom rendering, adding accessibility, and more. You can also modify the data, move the camera, listen to events, and change the rendering in any way you wish, adding an extra level of interaction with the graph.
We’ve seen a collection of the top 10 Javascript AI libraries covering all aspects of machine learning and data science. While JavaScript isn’t as popular in these areas as compared to Python or R, it’s becoming more prominent these days. For example, D3 is a very important and well-known library in data visualization. So, take a look at all of these libraries, and who knows, you might find them useful for your next project in machine learning or data science.
