Machine learning is a subfield of Artificial Intelligence (AI) that gives computers the ability to learn without being explicitly programmed. A lot of traditional computer programming involves coders instructing specific machines to take certain actions, but machine learning involves generating algorithms that can tell the computer how to make decisions on its own.
If you're looking for ways to start using this technology and explore its capabilities, These are the best Javascript Machine Learning libraries that are currently used in real-world projects by companies.
1.ML5.JS
ml5.js is an open-source, friendly high-level interface to TensorFlow.js, a library for handling GPU-accelerated mathematical operations and memory management for machine learning algorithms.
ml5.js aims to make machine learning systems more accessible for artists, coders, and students alike. With the system, people will be able to experiment with cutting-edge machine learning algorithms & models right in their browser.
ml5.js provides access to pre-trained models for detecting human poses, generating text, styling an image with another, composing music, pitch detection, and common English language analysis.
Website: https://ml5js.org/
2. TensorFlow.js
TensorFlow.js is an open-source software library from Google that lets you train and deploy machine learning models in a browser as well as on the server. TensorFlow.js lets you build and train models in JavaScript using flexible and intuitive APIs.
With TensorFlow.js, you can use any pre-built JavaScript model or convert a model written in Python to run on the web or with Node.js. This JavaScript machine learning library enables you to train models from scratch or retrain historical ML models with your own data.
Website: https://www.tensorflow.org/js
3. Brain.js
Brain.js is a JavaScript Machine Learning library that can be used to do symbolic algebra and machine learning. Brain.js is a library of tools to build Artificial Neural Networks and Deep Learning systems. It can be used both as a standalone library or in conjunction with Javascript code. The architecture is also modular, so you can use only small pieces of it or the whole package.
Brain.js does computations with GPU as well as a fallback to pure JavaScript when GPU is not available. Brain.js provides multiple neural network implementations which can be trained to do different tasks easily.
You can easily export and import trained models using JSON format or as a function, and host pre-trained models on your website easily.
Website: https://brain.js.org/
4.Keras.js
Keras.js is a library for working with neural networks on the web browser. This library wraps such things as training models, making predictions, or just displaying examples of models - in an intuitive and easy-to-use HTML/JavaScript user interface. Aside from requiring absolutely no back-end language skills, it's also fast: implementations are possible either synchronously or asynchronously.
Website: https://transcranial.github.io/keras-js/
5.ConvNetJS
ConvNetJS is a Javascript library that allows people to use their browser as a computer by teaching them how to train. This process is made without any need to go through hoops and hurdles.
ConvNetJS is a JavaScript library for implementing Convolutional Neural Networks. A convolutional neural network (CNN) is a kind of deep artificial neural network that is designed to recognize patterns in the data. It's well-suited for analyzing time-series and does feature extraction through activation maximization.
Website: https://cs.stanford.edu/people/karpathy/convnetjs/index.html
