Top AI/ML Libraries for JavaScript In 2021
How convenient is it to find relevant titles on Netflix after finishing a beloved series to keep you entertained? Or when you are buying a smartphone on Amazon and see product pages for phone cover right below it or at times even in custom-made packages to get some sweet discounts!? The magic behind this everyday convenience that we, at times, take for granted, comes from the concept of Machine Learning. Machine Learning is a broader concept than picking your next binge-worthy series; it is a vast field of opportunities and future-ready solutions. Voice navigation and using personal assistants in the form of Google Assistant and Siri on Android and Apple smartphones, respectively, is also machine learning.
So what exactly is machine learning? Machine Learning is a subset of Artificial Intelligence that empowers any system to understand and optimize processes without being consistently programmed. Machine Learning uses data, statistics, and trial and error methods to ‘learn’ a specific task without being coded to perform that particular task.
Web applications are the future of creating smart, innovative, modern and future-ready solutions for businesses to captivate customers’ attention and interest. JavaScript is one of the leading programming languages used by most app developers for creating web applications. Hence integrating machine learning capabilities in JavaScript is the sure way of getting your web applications future-ready and competent in this intense competition-driven market.
Here are the Top AI ML Libraries for JavaScript that are going to be hot favourites in 2021
1. TensorFlow
GitHub — https://github.com/tensorflow/tensorflow
NPM — https://www.npmjs.com/package/@tensorflow/tfjs
TensorFlow is an end-to-end open-source machine learning platform and a JavaScript library for training and deploying deep learning models in the browser and NodeJS. This AI ML JavaScript Library aims to create machine learning models for mobile, desktop, cloud and web easier for beginners and experts. At its core, Tensorflow is a dataflow programming library. It leverages several optimization techniques for making complex mathematical calculations and expressions more accessible and more performant.
Key Features of TensorFlow –
- Great support for deep neural networks and machine learning concepts.
- High scalability for computational skills across the machine and huge data sets.
- GPU/CPU computing where the same code can be executed on both architectures.
- Efficient flow with mathematical expressions that have multi-dimensional arrays.
Key Use Areas –
Voice/Sound Recognition
- Voice Recognition
- Voice Search
- Sentiment Analysis
- Flaw Detection
Text-Based Apps
- Language Detection
- Text Summarization
- Smart Reply
Image Recognition
Time Series
- Recommendation
Video Detection
- Motion Detection, Real-Time Thread Detection, Security, Airports
2. BrainJS
GitHub — https://github.com/brainjs/brain.js
NPM — https://www.npmjs.com/package/brain.js?activeTab=readme
BrainJS is a JavaScript written, GPU accelerated library of neural networks. The library is easy to use and performs computations using GPU and fallback to pure JS if GPU is unavailable. Having multiple neural network implementations, BrainJS allows developers to train different neural nets to do other things.
Key Features of BrainJS –
- Creating a simple neural network in high-level languages to leverage huge numbers of open-source libraries.
- Creating a variety of neural networks such as — Feedforward NN, Recurrent NN, Long Short Term Memory NN and more.
Key Use Areas –
- Building and training Neural Networks
- Creating Node Applications
- Useful for creating In-Browser Games
- Ad Placements
- Character Recognition
3. ConvNetJS
GitHub — https://github.com/karpathy/convnetjs
NPM — https://www.npmjs.com/package/convnetjs
ConvNetJS is a JavaScript library used for Deep Learning Models/Neural Networks entirely in the browser. A researcher at Stanford University wrote it. ConvNetJS allows developers to formulate and solve Neural Networks and Deep Learning in JS without any software dependency. All you need is a browser to make use of this AI ML JavaScript library. It has modules related to Common Neural Networks that have non-line rarities and fully connected layers.
Key Features of ConvNetJS –
- Supports Regression (L2) cost functions and Classification (SVM/Softmax).
- Can be used to train and specify Convolutional Networks that process images.
- Supports experimental Reinforcement Learning module based on Deep Q Learning.
Key Use Areas –
- Training a convolutional network for images.
- Training a reinforcement learning agent.
- Neural net classification.
- Neural Net Regression.
4. stdlib
Github — https://github.com/stdlib/lib
NPM — https://www.npmjs.com/package/@stdlib/stdlib
Stdlib is short for Standard Lib that is a standard library for JS and NodeJS. It offers a range of scientific and numerical calculation capabilities for your machine learning applications. This library has a wide array of mathematical functions that help build statistical models, data visualization capabilities for transforming your data into appealing visuals that make extracting insights easier. The library also has other utilities to make the app development and library development process easier. Stdlib can be considered as a great all-in-one general purpose package that comes packed with extensive documentation and benchmarks for helping you develop your machine learning.
Key Features of stdlib –
- Helps developers leverage high performing, rigorous and robust statistical and mathematical function.
- Has plotting and graph functionality that helps developer visualize their data.
- Has functions to assert, group, map, filter, pluck, and transform developers’ data both on the browser and the server.
Key Use Area –
- Has Data Visualization capabilities.
- Helps developers build Statistical Models.
- Helps in easing App Development and Library Development Process.
5. Mind
GitHub — https://github.com/stevenmiller888/mind
NPM — https://www.npmjs.com/package/node-mind
Mind is a flexible JavaScript-based neural network library for NodeJS and browser. This library makes use of a matrix implementation for efficiently process training data. It allows developers to personalize the network topology. Mind is also pluggable, making it easy for developers to upload or download the plugins with ease for configuring pre-trained networks that can be used to create certain predictions.
Key Features of Mind –
- Uses a matrix implementation to process training data.
- It is pluggable and hence can download/upload minds that have previously learned.
- It allows you to customize the network topology.
Key Use Area -
- Training neural networks.
6. Neataptic –
GitHub — https://github.com/wagenaartje/neataptic
NPM — https://www.npmjs.com/package/neataptic
Neataptic is a popular AI ML JavaScript library that has flexible neural networks. It helps remove synapses and neurons with a single code line. There is no fixed architecture needed for these neural networks to function at all. You can shape and reshape them for your dataset leveraging neuro-evolution, which is executed using multiple threads.
Key Features of Neataptic –
- Allows developers to create feed-forward neural networks.
- It helps conduct accurate time series prediction by utilizing previous inputs and their corresponding output values as the next input to the hidden layer.
Key Use Area -
- Can be used for training neural networks.
- Can be used for neural network normalization.
- Can be used for network visualization.
- Can be used for network evolution.
To Conclude
These are the top AI ML Libraries for JavaScript that can help you with various aspects of your project requirements. Keep exploring more AIML libraries for enhancing JavaScript capabilities and implementing machine learning in web app development. JavaScript is emerging as one of the most prominent languages used with subjects like deep learning and machine learning. Since JS is one of the most popular base programming languages, it brings incredible convenience for developers to work with algorithms and develop new solutions for many challenges.
