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.
Table of Contents
1. TensorFlow.js library
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
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
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
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
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.