Anom Warbhuvan
~ 5 min read | Published on Jan 24, 2024
TABLE OF CONTENT
- 1. TensorFlow.js
- 2. ML.js
- 3. Neuro.js
- 4. Brain.js
- 5. OpenCV.js
- 6. ML5.js
- 7. WebDNN
- 8. Synaptic.js
- 9. Compromise
- 10. D3.js
- 11. Tracking.js
- 12. ConvNet.js
- 13. Danfo.js
- 14. JSFeat
- 15. Keras.js
- 16. NLP.js
- 17. Mind.js
- 18. Sigma.js
- 19. Face-api.js
- 20. Magneta.js
- 21. Webgazer.js
- 22. Wink
- 23. MachineLearn.js
- 24. Natural
- Conclusion
In the past, programming and machine learning were two distinct domains with unique languages and tools. As machine learning is so much more math-heavy and technical than programming languages, it needed a specialized skill set for implementation.
However, the emergence of Javascript machine learning libraries changed the game. With these libraries, developers can bring machine learning capability into their web apps. Enabling you to build power intelligent applications with neural networks, train them on vast datasets, and make predictions and decisions within their Javascript code.
In this article, we will go through the different javascript libraries for Machine Learning (ML), Natural Language Programming (NLP), and JavaScript together.
1. TensorFlow.js
TensorFlow.js is an open-source library for building and training machine learning models in JavaScript. It provides a comprehensive set of tools for building and training machine learning models in the browser or in a JavaScript runtime environment.
TensorFlow.js supports a wide range of machine learning tasks, including image classification, language translation, and reinforcement learning. It can be used for tasks related to NLP, such as sentiment analysis, language translation, and text generation.
Model created with Tensorflow can recognize objects in images, handwritten characters, and faces in photos. Nowadays it is also being used to predict stock market trends, customer behavior, and disease outbreaks.
2. ML.js
ML.js offers a rich API that enables developers to build and train machine learning models with just a few lines of code.
You can use ML.js to run common machine learning tasks like classification, regression, clustering, and dimensionality reduction. This library also includes data-related tools such as data preprocessing, feature extraction, and data visualization, helping one achieve complex ML functions.
ML.js has wide applications and is used for natural language processing, computer vision, and recommendation systems. This library focuses on readability and integrates easily with our existing projects. It is also supported by a community of developers and researchers who are actively developing and maintaining ML.js.
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3. Neuro.js
Neuro.js is a Javascript library that focuses on natural language processing that helps in creating AI assistants and chatbots. It trains neural networks such as Feedforward Networks, Recurrent Networks, and Convolutional Networks.
You can define a neural network architecture with Neuro.js by using a simple and intuitive API. The number of layers, the number of neurons in each layer, the activation functions, and the loss functions can all be customized.
Overall, Neuro.js is a powerful and easy-to-use library that is appropriate for both beginners and advanced users who want to experiment with machine learning in the browser or Node.js.
4. Brain.js
Brain.js, is a Javascript library for machine learning used for building and training neural networks in the browser or on Node.js. It comes with a simple and intuitive interface and supports a wide range of neural network architectures, including Feedforward Networks, Recurrent Networks, and Long and Short Term Memory (LSTM) Networks.
It is easy to integrate it with a wide range of development environments and platforms due to its cross-platform compatibility. Brain.js is suitable for projects that require fast and efficient training of neural networks. You can run it in any browser or Node.js environment which makes it ideal for any real-time application such as game AI, web applications, and even for apps that require low latency.
5. OpenCV.js
OpenCV.js is a Javascript port of the popular open-source computer vision and machine learning software library OpenCV. It includes a collection of powerful tools and algorithms for processing and analyzing images and video in the browser or Node.js environment.
You can handle image filtering, feature detection, object recognition, face detection, and optical flow analysis tasks using OpenCV.js. Machine learning algorithms such as decision trees, random forests, and vector machines are also supported.
OpenCV.js can be used in both client-side and server-side applications. Use its API to integrate computer vision capabilities into web applications.
6. ML5.js
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 TensorFlow. ML5.js is a community-driven library and is suitable for beginners as it provides a deep understanding of fields such as ethical computing and data collection.
You can add your dependencies when working with a Node.js environment, and solve both supervised and unsupervised problems. Developers can incorporate javascript ML libraries into their projects without building models from scratch. It supports creative applications of machine learning, such as generative art, music and design.
7. WebDNN
WebDNN is an open-source deep learning framework that enables the execution of deep neural networks in the browser. It includes a cross-platform runtime engine that can run deep learning models on desktops, laptops, smartphones, and embedded devices.
One of WebDNN’s key features is its ability to execute on pre-trained models like Tensorflow, Keras, and PyTorch. WebDNN also includes a set of APIs for loading and running converted deep learning models in the web browser. Another benefit of WebDNN is that it supports hardware acceleration. It can make use of hardware acceleration technologies like WebGL and WebGPU to improve the performance of deep learning models.
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8. Synaptic.js
Synaptic is a lightweight and flexible JavaScript library for building neural networks. This library provides a fast and efficient implementation of neural networks. It allows you to train each specific neural network using tests such as the built-in Reber Grammar test, XOR solving, and distracted sequence recall task completion.
It is designed for users to easily create, combine, and reuse different types of neural network components. It also helps in building client-side predictive modelling and deploying models without a server. It can be used in educational settings to demonstrate the basic concepts of neural networks and machine learning.
9. Compromise
Compromise.js is a Node.js and browser-based natural language processing library. It provides a set of tools for parsing, comprehending, and manipulating English text. Similar to NLP.js, it also provides a plugin system that allows you to extend its functionality with your custom modules.
With Compromise, you can easily extract text information such as nouns, verbs, adjectives, dates, times, and addresses. Not just this, you can also carry out text operations like pluralization, capitalization, contraction or expansion. It has sentiment analysis, named entity recognition, part-of-speech tagging, and verb conjugation.
10. D3.js
D3.js stands for Data-Driven Documents. It enables developers to create dynamic and interactive web data visualizations by incorporating bar charts, line charts, scatterplots, and interactive maps. Data filtering, data binding, and data manipulation are some powerful tasks that you can accomplish with D3.js.
You can use it to create highly responsive dynamic visualizations by combining SVG (Scalable Vector Graphics) and HTML elements. D3.js also includes a variety of layout algorithms for creating more complex visualizations like hierarchical layouts and network graphs.
It is primarily used in data visualization in journalism, and for academic work.
11. Tracking.js
Tracking.js is a computer vision Javascript library that allows you to implement various CV algorithms in your browser.
It supports object and color tracking, feature detection, convolution, grayscale, image blur, and other algorithms. It is used to detect and track object faces in real-time, that too without any special hardware or software. Making it appropriate for a variety of applications, including augmented reality, motion detection, and interactive games and applications.
It also easily integrates with other machine learning and computer vision libraries like OpenCV.js and dlib.
12. ConvNet.js
ConvNet.js is a deep learning Javascript library for the browser. This ML library can solve neural networks using Javascript and supports popular network modules along with regression (L2) cost functions and classification (SVM/Softmax). It is also completely browser dependent and has no dependency on software like GPU or compilers.
This trained neural network specifies images when working with convolutional networks. It supports an experimental reinforcement learning module built on Deep Q Learning. It has fully connected layers which do not contain linearities. Making it the right machine learning library for neural network regression.
13. Danfo.js
Danfo.js is a Javascript machine learning library for web-based data analysis and manipulation. Data tools include data cleaning, transformation, and analysis, which are designed to be accessible and simple to use by developers.
It has several data structures that enable developers to interact with data in a way that is appropriate and familiar to users of well-known data analysis tools, such DataFrames and Series. It works on large datasets and can efficiently handle millions of rows, making it a data-intensive application.
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14. JSFeat
JSFeat is a JavaScript Machine Learning library that provides image processing algorithms for computer vision in the browser. It is super efficient when it comes to performing tasks like feature detection, image filtering, and object recognition. Its performance is optimized for both desktop and mobile devices, and it can handle large images with high accuracy.
Real-time image processing and analysis tasks can be performed directly in the browser, with no server-side processing. JSFeat includes feature detection algorithms like ORB and FAST corner detection, and image filtering algorithms like edge detection and blur.
15. Keras.js
Keras.js enables pre-trained deep learning models to be deployed in a web browser or JavaScript environment and provides high-level APIs for building, training, and running machine learning models, similar to the Python-based Keras library. It runs efficiently in browsers with GPU support via WebGL.
This machine learning library is versatile and supports multiple deep learning models and architectures. It does bidirectional long short-term memory for Internet movie database sentiment classification. Many cabs and over-the-top service platforms like Uber and Netflix have started using Keras.js as a part of neural networking to enhance their user experience.
16. NLP.js
NLP.js is an open-source natural language processing library. It includes tools for tokenization, stemming, part-of-speech tagging, named entity recognition, sentiment analysis, and text classification. It has an API for integrating with other tools and services like voice assistants, chat platforms, and content management systems.
It uses machine learning to understand human language and create chatbots, virtual assistants, and other language-based Javascript applications. The library includes pre-trained models for a variety of languages, including English, Spanish, French, Italian, and Portuguese.
17. Mind.js
Mind.js is a Javascript machine learning library for machine learning, built on top of TensorFlow.js. It provides an interface for performing operations on arrays of data and allows developers to perform machine learning tasks like training and inference directly in the browser or on Node.js servers.
It uses matrix implementation for processing training data and uses it to make better predictions.Mind.js is used in robotics for autonomous navigation and object recognition, in healthcare for diagnosis and treatment recommendation, and in retail for object detection in images.
18. Sigma.js
Sigma.js is a Javascript machine learning library used in web browsers to display and manipulate interactive graphs and networks. It is widely used in academic research and data journalism because it provides a flexible and powerful platform for creating visualizations of complex networks.
You can generate graph visualizations such as node-link diagrams, matrix visualizations, and force-directed layouts. It supports many interactive features, including zooming and panning, node and edge highlighting, and edge bundling. It can be combined with other web technologies such as React, Angular, and Vue.js.
19. Face-api.js
Face-api.js detects faces in images and videos and identifies various facial attributes such as gender, age, and emotion. It also includes a powerful face recognition API for recognizing and identifying individuals in images and videos. It can be used for a variety of purposes, including security and surveillance systems, social media analytics, and interactive games.
Face-api.js is notable for its speed and accuracy. It is capable of real-time image and video processing, as well as handling large datasets of faces with high accuracy. It also has an easy-to-use API for incorporating face detection and recognition capabilities into your web applications.
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20. Magneta.js
Magneta.js is a Javascript machine learning library built entirely with pre-trained interference models and supports GPU acceleration. It is a Google Brain project that explores the role of machine learning in the creation of art and music. Magneta.js provides a high-level API for generating musical sequences, melodies, and more using machine learning models.
MelodyRNN, DrumsRNN, and ImprovRNN are among the pre-trained models in the library that can be used to generate music. Magenta.js also includes visualization tools for the generated music and integrates with popular music software like Ableton Live and Max/MSP. It has a wide range of applications, ranging from music production to interactive art installations.
21. Webgazer.js
Webgazer.js is an eye-tracking Javascript library that uses a webcam to infer a visitor’s eye gaze. Eye tracking can be used in your web applications for a variety of purposes, including user experience research to make games and applications interactive.
It works by calibrating the user’s gaze position using a simple calibration procedure that requires the user to look at a series of dots on the screen. The library, once calibrated, can track the user’s gaze position with high accuracy, even if the user moves their head or the camera angle changes.
22. Wink
Wink.js is a Javascript library that can help you with various NLP tasks. It includes text-related utilities, such as string manipulation, regular expressions, and data cleaning. Its modular architecture gives developers the freedom to pick and use only libraries and functionalities that they need for the project.
Wink.js has a versatile set of powerful natural language processing (NLP) and javascript ML libraries that can be used in chatbots, sentiment analysis tools, and text classifiers.
23. MachineLearn.js
MachineLearn.js provides machine learning algorithms for SVM, linear models, decision trees, clustering, and other utilities. It is GPU accelerated and binds with native C++.
It provides APIs for running algorithms in the browser and comes with datasets like Boston, heart disease, Iris, and others.
24. Natural
Natural is a Node.js based natural language processing library that supports a wide range of operations including tokenization, stemming, tf-idf, position tagging, sentiment analysis, and spellchecks.
It is still in the testing phase until it is fully integrated with Wordnet.
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Conclusion
In this article, we have shared some of the best Javascript machine learning libraries that can help you visualize data, track eye movements, and perform many other complex tasks.
So supercharge your Javascript-based project with machine learning features, with these libraries. Choose the one that meets your project requirements and get started.
Happy Coding!
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