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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.