JavaScript is the programming language of the web which makes it pretty important! However, it has mostly been used as a scripting language in web development without much association with Machine Learning or Data Science as compared to R and Python. That’s because R and Python are specifically suited to Data Science or ML with a large collection of supporting libraries, community members, and infrastructure. However, in the past few years, JavaScript’s popularity has increased to such an extent that more and more people are going crazy over it! That’s why this article deals with the top 10 JavaScript libraries that are quite popular these days.
These include many awesome JavaScript libraries now for different implementations of Machine Learning and Data Science such as nlp.js or Compromise for Natural Language Processing, D3.js or Chart.js for Data Visualization, and Brain.js, TensorFlow.js, etc. for uses in generic machine learning. You can implement all these facets of Machine Learning or Data Science in JavaScript using these libraries both in the browser and at the backend using Node.js. So without further ado, let’s check out these libraries now.
Javascript Libraries for Machine Learning:
1. Brain.js
Brain.js is a javascript library for machine learning and neural networks in particular. It is quite fast as it uses GPU for computations and also has the capacity to revert back to pure JavaScript when GPU is not available. Brain.js provides the implementations for various types of neural networks and the best thing is you don’t need to be deeply familiar with neural nets to use this library. You can also import these models as a function or in a JSON format and integrate them into your website.
2. TensorFlow.js
TensorFlow.js is a Machine Learning library in javascript that has a comprehensive, flexible variety of tools, libraries, and resources for Machine Learning. You can run the official TensorFlow models that are already available or you can convert your Python models as well. There are also preexisting machine learning models that you can retrain using your own data. You can also deploy the machine learning models anywhere including the cloud, the browser, on-premises, or on the device regardless of the language you use. However, TensorFlow.js is just one version of TensorFlow with many other options available such as TensorFlow Lite for mobile devices, TensorFlow Extended for the full experience, TensorFlow Rust for Rust bindings, etc.
3. Synaptic
Synaptic is a JavaScript neural network library that is created for node.js and the browser. The networks can be either imported or exported to JSON as standalone functions as well. They can be connected to other networks or even gate connections. The library also has many useful build-in architectures like liquid state 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 along with it. Synaptic is also an open-source library from MIT so anyone can contribute or use it for free.
4. ConvNetJS
ConvNetJS is a javascript library specifically dedicated to training deep learning models that include neural networks. The big advantage of this library is that it can be used entirely in the browser with no special software requirements such as GPUs, compilers, etc. ConvNetJS has options for neural networks, classification and regression problems, convolutional networks that are focused on images, and a Reinforcement Learning module that is in the experimental stages.
5. ml5.js
ml5.js is a javascript machine learning library that is based on top of TensorFlow with no other external dependencies. It allows access to various machine learning pre-trained algorithms in the browser that are used for detecting human poses, detecting pitch, styling an image with another, generating text, finding English language word relationships, composing music, etc. ml5.js has a particular focus on providing a deeper understanding of machine learning to people along with its complexities like responsible data collection, ethical computing, etc.
Natural Language Processing:
1. nlp.js
nlp.js provides a javascript-based natural language utility for nodejs. It has many different functionalities like guessing the language of a phrase or obtaining the stemmers and tokenizers for different languages. nlp.js is also capable of sentiment analysis for different phrases written in a particular language. You can also classify the intent of any sentence and then generate an answer for the sentence based on the intent using the Natural Language Processing Classifier and the Natural Language Generation Manager respectively. nlp.js has native support for 40 languages while it can support an additional 104 languages with BERT integration.
2. Compromise
Compromise is a JavaScript library that is specifically focused on natural language processing so that it is easier to interpret and pre-parse the text to make decisions based on the text. Compromise can compress a lot of words and expand them at the runtime so that the assumptions can be obtained. Around 99.99% of all the vocabulary in English can be handled by 14,000 words which are compressed into a file size of just 40kb. This makes compromise very fast in understanding and scanning the words with latency in low milliseconds.
Javascript Libraries for Data Science and Visualization:
1. D3.js
D3 or Data-driven documents is a JavaScript library that can be used to manipulate the data using HTML, CSS, and SVG to obtain custom data visualizations. D3 has the capacity to combine documents with a Document object model and then transform the document based on the requirements. D3 also has different chart types for data analysis like box plots, and histograms, for hierarchies like treemaps, for networks like chard graphs, as well as common charts like scatter plots, line charts, bar charts, pie charts, etc. D3 also provides animation options like an animated treemap, zoomable bar charts, icicles, bar chart races, etc.
2. Chart.js
Chart.js is an open-source javascript charting library that provides 8 broad chart types that include all the common charts such as bar charts, pie charts, histograms, scatterplots, error charts, etc. All these charts can be combined to produce mixed charts that are customizable and able to be animated as well. Chart.js can also render easily across all web browsers and adjusts the chart according to the window size on the web browser. All the charts in this library can also be combined with the moment.js library if a time axis is needed.
3. Sigma.js
Graphs are a very important part of data visualization and sigma.js is specifically focused on graph drawing. It has built-in features that simplify graph visualization and publishing on web pages. Sigma.js has Canvas and WebGL support as well as options for mouse and touch support, custom rendering, added accessibility, etc. You can also modify the data, move your camera, listen to events, and change the rendering in any manner you wish to add extra levels of interactivity with the graphs.
Conclusion
We have seen the top 10 JavaScript libraries that cover the various facets of Machine Learning and Data Science. While JavaScript is not as popular in these fields as compared to Python or R, it is becoming more and more prominent these days. For example, D3 is a pretty important and famous library in data visualization. So check out all these libraries, and who knows, you might find them useful for your next project in Machine Learning or Data Science.
