In the past, programming and machine learning were two separate fields with unique languages and tools. And because machine learning is much more specialized and mathematically oriented than programming languages, it required a special set of skills to implement.

The advent of machine learning libraries in JavaScript changed the game. With these libraries, developers can add machine-learning capabilities to web applications. They will also have the possibility to build smart and powerful applications using neural networks. And train those applications with vast datasets.

In this article, we will review several different JavaScript libraries for machine learning (ML) and natural language processing (NLP). In future articles, we will complete this list.

JavaScript machine learning libraries

1. TensorFlow.js library

TensorFlow.js is an open-source library for building and training machine learning models in JavaScript. And provides a comprehensive set of tools for building and training machine learning models in the browser or a JavaScript Runtime Environment.

The JavaScript runtime environment is software that provides access to internal libraries and objects that are available to an application so that it can interact with the outside world and execute code.

TensorFlow.js supports a wide range of machine learning tasks, including image classification, language translation, and reinforcement learning (RL). And it is used for NLP-related things like sentiment analysis and text generation.

Reinforcement learning is a subset of machine learning that allows an AI-based system to learn through trial and error using feedback from its actions.

The model created with TensorFlow recognizes objects in images and handwritten characters, as well as faces in photos. Today, this library is also used to predict disease outbreaks, stock market trends, and customer behavior.

2. ML.js library

ML.js provides a valuable API that enables web developers to build and train machine learning models with just a few lines of code. You can use ML.js to perform common machine learning tasks such as classification, regression, clustering, and dimensionality reduction. The library also includes data-related tools such as data preprocessing, feature extraction, and data visualization that help achieve complex ML functions.

ML.js has wide applications and is used for natural language processing (NLP), computer vision, and recommender systems. This library focuses on readability. And it easily integrates with the developer’s existing projects. Another advantage of this library is that it is supported by a community of web developers and researchers who are actively developing and maintaining ML.js.

3. Neuro.js library

Neuro.js is a JavaScript library focused on natural language processing (NLP). NLP helps create artificial intelligence assistants and chatbots. This library teaches neural networks such as Feedforward Networks, Recurrent Networks, and Convolutional Networks.

You can define a neural network architecture with this library using a simple and intuitive API. The number of layers, the number of neurons in each layer, activation functions, and loss functions are all customizable.

Overall, Neuro.js is a powerful and user-friendly library. This library is suitable for both beginners and advanced users who want to experience machine learning in the browser or Node.js.

4. Brain.js library

Brain.js is a JavaScript library for machine learning that is used to build and train neural networks in the browser or Node.js. This library has a simple and tangible user interface. And it supports a wide variety of neural network architectures, including feedforward networks, recurrent networks, and long-short-term memory (LSTM) networks.

Integrating this library with a wide variety of development environments and platforms is easy due to its cross-platform compatibility. Brain.js is suitable for projects that require fast and efficient training of neural networks. Also, this library can be implemented in any browser or Node.js environment. This is the feature that makes Brain.js ideal for any real-time application such as Game AI, web applications, and even for applications that must have low latency.

5. OpenCV.js library

OpenCV.js is a JavaScript port of the popular OpenCV computer vision and machine learning library. This library includes a set of powerful tools and algorithms for processing and analyzing images and videos in a browser or Node.js environment.

Using OpenCV.js makes it possible to apply filters on images, feature detection, object detection, face detection, and optical flow analysis. Also, this library supports machine learning algorithms such as decision trees, random forests, and vector machines.

OpenCV.js is used in both client-side and server-side applications. Its API is also used to integrate computer vision capabilities into web applications.

6. ML5.js library

ML5.js is a high-level JavaScript library for training and using machine learning models in the browser. This library supports machine learning tasks similar to the TensorFlow library. ML5.js is a community-oriented library and is suitable for beginners. Because it emphasizes areas such as ethics in the computer world and data collection.

Dependencies can be added when working with the Node.js environment. Developers can incorporate JavaScript ML libraries into their projects without having to build the model from scratch. It supports creative machine-learning applications such as generative art, music, and design.

7. WebDNN library

WebDNN is an open-source deep learning framework that enables running deep neural networks in the browser. And it includes a cross-platform runtime engine that can run deep learning models on desktops, laptops, smartphones, and embedded devices.

One of the key features of WebDNN is its ability to run on pre-trained models such as Tensorflow, Keras, and PyTorch. WebDNN also includes a set of APIs for loading and running transformed deep learning models in a web browser. Another advantage of WebDNN is that it supports hardware acceleration and uses technologies such as WebGL and WebGPU to improve the performance of deep learning models.

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