Top Javascript Machine Learning Development Frameworks & Libraries

Artificial Intelligence (AI) is propelling new business opportunities across industries. With rapid algorithmic advancements, businesses are able to harness artificial intelligence services to embed automation in critical operations and processes. The development of AI-powered solutions is accelerated with emerging javascript machine learning development frameworks and libraries. These mechanisms are effective at training the machine learning models for domain-specific automated solutions for complex business setups. 

Let’s explore some wide-ranging javascript-based machine learning libraries that can be deployed for diverse projects. 

Brain.js:

Brain.js is a node library written in JavaScript for neural networks that can be used with Node.js for the ML applications. It also has support for asynchronous tasks to train neural network data using trainAsync() and support for streams as well.

ml.js:

Mljs is an organization that developed ml.js library. This library is a collection of tools developed by the mljs organization. It includes a vast collection of tools for different categories such as unsupervised learning, supervised learning, artificial neural networks, regression, optimization, statistics, data processing, and math utilities.

Synaptic:

Synaptic is a Javascript neural network library that has a few built-in architectures. It can train the networks like multilayer perceptrons, liquid state machines or Hopfield networks, and a trainer capable of training different networks. Synaptic library browser which enables you to train first and even second-order neural network architectures. 

Conventjs:

The ConvNetJS JavaScript implementation of neural networks that are developed by a Stanford University Ph.D. It currently supports common neural network modules, SVM, regression, and the ability to train convolutional networks to process images.

ConvNetJS implementation of neural networks supported modules are 

1. Classification regression

2. Experimental Reinforcement Learning module

3. Convolutional networks that process images.

Deeplearnjs:

Deeplearnjs library provides the capability to train neural networks in a browser and run pre-trained models in interface modes, and also claims to be used as a Numpy for the web. It's easy to pick up an API and used for a variety of useful mil applications and is actively maintained.

TensorFlow:

TensorFlow is an end-to-end open-source library for machine learning and data processing. It has a comprehensive, flexible ecosystem for numerical computation and large-scale machine learning and developers easily build their massive datasets to give users the best experience for ML-powered applications. Also, the combination of Keras and TensorFlow libraries can be harnessed by the providers of chatbot development services to build contextual and voice-based chatbots for maximum customer engagement. 

Conclusion:

JavaScript is still in its embryonic stage. However, the above-mentioned libraries are beginning to automate key business verticals with efficient machine learning applications. In addition, developers are experimenting with libraries built using C, LIBSVM, LIBLINEAR, and other mechanisms. These can be implemented in Node.js too, using native extensions provided by the Node.js core APIs.