JavaScript is one of the widely used programming languages for the development of websites and applications. Among all the JavaScript frameworks, Nodejs is the most popular. According to the latest stats, about 47.12 percent of developers around the globe leverage NodeJS libraries.
Today, machine learning has become a trending technology with wide applications in various industries. Therefore, the development of machine learning applications has increased. Want to hire Node JS developers and build machine learning apps? In that case, you must have an idea of the libraries and tools to leverage.
This blog will provide you with detailed insights into the top 10 NodeJS libraries and tools for machine learning. Let’s take a look!
1. BrainJS
When it comes to NodeJS libraries and tools for machine learning, BrainJS tops the list. It is one of the most efficient JavaScript-written libraries. It provides the developers with several networks for different tasks. You can leverage this library for both the web browser and the NodeJS environment.
The best thing about this NodeJS library is that it is easy to use and fast. The simple execution makes it ideal for machine learning projects. It allows you to create interesting functionalities with the use of a reliable dataset and a few code lines. Moreover, it enables developers to access a wide range of open-source libraries in a hassle-free manner.
2. MLJS
MLJS is another popular NodeJS library for machine learning. It offers you easy access to machine learning algorithms and models in web browsers. It consists of a long list of libraries under various categories like artificial neural networks, data processing, supervised learning, math utilities, optimization, regression, and more.
All you need to do is select the right library as per your specific requirement. The library also supports routines such as arrays, hash tables, statistics, linear algebra, cross-validation, and more. The best thing about MLJS is that it is an open-source library and easy to use.
3. Synaptic
Synaptic is a JavaScript-based library that has been developed by MIT. The architecture-free algorithm and pre-manufactured structure are the main features that make the library appealing to the developers. It allows professional developers to efficiently build and train the first or second-order neural net architecture.
Even if you do not have in-depth knowledge about neural networks and machine learning techniques, you can conveniently use the Synaptic library. With the help of JSON format, you can also export and import ML models. The pre-defined networks are yet another interesting feature of the JavaScript library. In this library, you can find documentation for networks, layers, neurons, trainers, and architects.
4. Limdu.JS
Limdu.JS is also a popular NodeJS library for machine learning. It supports online learning, Binary classification, feature engineering, real-time classification, and multi-label classification. There are a number of use cases of this NodeJS library.
One of the important use cases of the library is serialization. If you are willing to train a classifier on the home computer and later use it on remote servers, serialization can prove to be beneficial.
5. TensorflowJS
TensorflowJS is an open-source cross-platform that helps in the implementation and development of machine learning and deep learning models. It allows you to leverage flexible APIs for the development of models using low-level JS linear algebra or high-level layer API. Therefore, TensorflowJS is among the popular libraries for machine learning projects.
One of the main benefits of using this platform is that it can be conveniently implemented on different hardware machines, such as cellular devices, computers, and more. Moreover, it frequently offers new features, allows quick updates, and ensures seamless performance. The developers also have the option to convert the Tensonflow models into Python models.
6. ConvNetJS
The ConvNetJS framework has been developed by a Stanford University PhD. At present, it supports neural network modules, regression, and SVM. Moreover, it comes with the ability to train convolutional networks for processing images.
The great documentation of ConvNetJS makes it highly preferred among developers. Other use cases of the framework include speed tests, image painting, and training an Autoencoder.
7. NeuroJS
NeuroJS is yet another important JavaScript framework for machine learning and deep learning. It can be easily leveraged for various neural network-based tasks. However, its main focus is on reinforcement learning. It comes with neat demos for better visualization of its capabilities.
NeuroJS can provide support for actor-critic models and deep-q-networks. It also helps in the implementation of a full-stack neural-network based machine learning framework. Developers can also use this framework for Binary export and import of network configurations. It ensures high performance and offers optimum value to the users.
The key benefit of NeuroJS is that it offers support for online learning, multi-label classification, and real-time classification. The simplicity of the library makes machine learning easily accessible and more practical.
8. KerasJS
Another important NodeJS library for machine learning is KerasJS. It is widely used by developers for the preparation and development of machine learning and deep learning models. The models developed with Keras run smoothly on the web applications.
In order to run the models, you will have to make use of the CPU mode. So, there isn’t any need for GPU acceleration. One of the major benefits of KerasJS is that it allows you to train the models in any backend.
KerasJS uses a number of frameworks for the backend. It enables the developers to train models in CNTK, TensorFlow, and much more.
9. Natural
When it comes to NodeJS libraries and tools for machine learning, you cannot forget Natural. It is a library that offers string similarity, classification, stemming, tokenization, WordNet, and phonetics. It provides you with language facilities to use modules in NodeJS.
The best thing about this library is that it can be easily installed without much hassle. However, a point to note here is that it is in its early stage. The majority of the algorithms are English-specific.
10. Stdlib
Stdlib is one of the huge JavaScript-based libraries. It is used by developers to create advanced machine learning libraries and mathematical models. However, developers can also use this library for plotting functionalities and conducting graphics. It finds wide application in data visualization and data analysis.
If you are willing to develop modular and scalable APIs, this library can prove to be an ideal option. It allows you to develop the APIs in much less time. The best thing about the Stdlib is that it provides you with robust mathematical and statistical functions.
Moreover, it can also offer easy sharing and control of APIs. It also has an auto-generated documentation facility.
Conclusion
The demand for NodeJS libraries for machine learning is on the rise. By now, you must have a comprehensive idea about the top NodeJS libraries and tools for machine learning. It is time to leverage the best ones according to your specific need and purpose. Make sure to use the right NodeJS library for the machine learning tasks.
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