#TensorFlow.js

Keras.js enables running Keras models in browsers with GPU acceleration via WebGL, compatible with backends like TensorFlow and CNTK. Despite being inactive, its demos, including MNIST CNN and ResNet-50, offer insights on model execution, supporting Node.js in CPU mode. For updates, refer to TensorFlow.js. Compatible with Keras 2.1.2.

NSFW Filter is a free, open-source extension blocking unsafe content for a secure browsing experience. Built with TypeScript and TensorFlow.js, it leverages NSFWJS for classifying images. Available on Chrome Web Store with easy installation and customization. Ideal for work-safe browsing, developers can contribute and test seamlessly through npm.

Yoha, a practical hand tracking engine, provides solutions for hand gesture recognition in various applications. The beta engine detects specific poses like pinch and fist, working through JavaScript for web use. Utilizing a custom neural network with TensorFlow.js, it offers real-time performance on desktops and limited functionality on mobile devices. Despite being unmaintained, developers can access demos and documentation for detailed understanding.

Make-sense is a user-friendly, web-based tool designed for photo labeling in deep learning. It runs directly in browsers without installation and is compatible with various operating systems. The tool integrates advanced AI, including YOLOv5 and TensorFlow.js, for task automation and data privacy protection. It supports multiple data export formats like CSV and COCO JSON and provides thorough documentation for local and Docker setups, offering a comprehensive solution for computer vision data preparation.

TensorFlow.js is an open-source library designed to train and deploy machine learning models using JavaScript. It enables developers to construct models with straightforward APIs in both browser and Node.js environments. The library supports executing existing TensorFlow models, retraining with client-side data, and using optimized backends like WebGL and WASM for improved performance. Its modular framework accommodates various platforms such as React Native and Node.js, providing tools for model visualization and conversion. This library offers an accessible solution for developers looking to implement machine learning in JavaScript.

This article examines the integration of TensorFlow.js in WeChat Mini Programs, focusing on enhancing AI features in mobile applications. It details setup requirements like NodeJS and WeChat Developer Tools for a smooth implementation. The project highlights the transition from modifying tfjs-core to using WeChat’s plugin system, solving issues such as inconsistent frame data across devices. It encourages open-source participation and community collaboration, offering insights into AI application potential.

This library seamlessly integrates TensorFlow.js to enable machine learning in the browser, tailored for artists and students. It provides a user-friendly interface focused on ethical computing, supported by tutorials and datasets. Inspired by Processing and p5.js, it fosters an inclusive community with clear guidelines and resources.