JavaScript is a popular programming language that’s known for powering thousands of web applications. JS frameworks such as Node.JS are a boon for developers to build and launch apps for specific platforms hassle-free. Taking a step ahead, the Node.JS framework’s libraries provide predefined methods and classes that enable developers to build modern apps at a speed that is stable and scalable.
With the growing demand for artificial intelligence and machine learning technology-based solutions, businesses are looking for quality service providers the use of some enhanced Node.JS libraries and tools are making it possible.
The company you partner with should provide quality mobile app development and web development services and also have AI & ML experts in the solution building team. Let’s take a look at all the top Node.JS libraries that are enabling machine learning application development efficiently.
Top 7 Node.JS libraries and tools leveraged for machine learning application development
Businesses can avail the services of machine learning engineers in the team to ensure that your product is launched in the market at the right time. The Node.JS libraries streamline the application development which we will discover in this section.
ML.JS library
The general-purpose machine learning JS library comprises various tools provided by ML.JS. The open-source library provides access to ML models in web browsers and performs various tasks such as supervised learning, unsupervised learning, optimization, regression, artificial neural networks, math utilities, and data processing.
When developers need to use ML.JS tools in the Node.JS environment, identifying the npm package is important. As every tool is available as a separate package, installing all of them simultaneously is not required.
Brain.JS
The Brain.JS library is leveraged for machine learning application development and neural networking is similar to the human brain. The library that can be used in both web browser and Node.JS- simplify the neural network process creation and training by easy JS utilization and restricting API to a few options and method.
Different types of networks for distinct tasks, several implementations on a neural network, and neural nets development on the server side provided by the Brain.JS library help in the faster development of the neural network.
Tensorflow.JS
Tensorflow.JS, the open-source popular Node.JS library is used for ML model deployment and training because of the flexibility it provides in API selection for module development. The hardware-accelerated library is well-famous, actively maintained, and enables GPU-based DL model writing, training, and deployment in JS.
With good computational graph visualization, Tensorflow.JS ensures high performance with faster updates, frequent releases, and new features. Plus, the library excels in easy implementation on various hardware from cellular devices to computers.
Synaptic
Synaptic, the JS neural network and machine learning library that works with a web browser- is written in JS language only. The built-in architectures such as LSTM, Hopfield network, multilayer perceptrons, and trainer for various networks training.
The architecture-free algorithm and pre-manufactured structure make it easier to train and build neural net architecture. It’s possible to access and use this library for ML development and neural network building without having comprehensive details of the same. Also, it helps with ML models import and export leveraging JSON format.
Stdlib
If you are looking for advanced statistical model development, exploratory data analysis, and graphics functionality for data visualization, or plotting, the stidlib library is the best Node.JS library. The library supports tooling, binary classification, linear regression, and NLP which brings various advantages to the development table.
The machine learning Node.JS library aces at data validation and feature detection with hundreds of assertion utilities, testing with a sample database, and data transformation and asynchronous control flow with general utilities. Easy bundling with various bundlers like- Webpack, Browserify, and others in web browsers reduces the developer’s work.
Keras.JS
Keras.JS, the neural network JS library is used for machine learning and deep learning models that generally run in a web app using CPU mode. One of the best Node.JS libraries handles abstraction comes from backend frameworks with support for high-level APIs.
The alternative to AI libraries limits GPU acceleration, but GPU support can be exploited with WebGL 3d design API. Keras takes an edge in training ML models in any backend.
Neuro.JS
Neuro.JS, the top Node.JS library is leveraged for deep learning and neural network as it put a laser focus on strengthening learning. It facilitates visualizing the capabilities with clear demos. Neuro also features high-performance rendering, and network configuration import and export.
With deep deterministic policy gradients, the support for actor-critic models and deep-q-network models is enabled which makes deep learning application development effortless.
Wrapping it up
Initially, JavaScript was not the primary option for artificial intelligence and machine learning application development. But today, NodeJS libraries and tools have proven to be a good fit for machine learning application development. In this blog, we have enlisted the top 7 JS libraries that are leveraged at scale for machine learning application development. Step into a machine learning application development game leveraging any of these JS libraries.
Still, if you have questions about which JS library to choose for your machine learning or deep learning integration, opting for NodeJS development services will help you know how to progress in machine learning with the right approach.
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