Awesome GitHub RepositoriesBrowser-Based Deep Learning

ConvNetJS is a JavaScript deep learning library and neural network training engine designed for client-side machine learning. It functions as a framework for building, training, and running convolutional neural networks directly within a web browser without the need for a backend server. The library specializes in image recognition and pattern analysis using convolutional and pooling layers. It enables the creation of models for classification and regression tasks, as well as the development of reinforcement learning agents that optimize behavior through trial and error in simulated environme

TensorFlow.js is a JavaScript machine learning library and browser-based runtime used to build, train, and execute models. It functions as a WebGL accelerated tensor engine, providing a foundation for high-performance linear algebra operations and an automatic differentiation framework for computing gradients. The project distinguishes itself through its ability to run machine learning directly in web environments, supporting both client-side inference and browser-based training. It enables the deployment of Python-based models by converting Keras or TensorFlow models into compatible formats

WebGPT is a browser-based machine learning framework designed to execute transformer models entirely within the client environment. By leveraging native web standards, it provides a zero-dependency runtime that enables local text generation without the need for backend server processing. The engine distinguishes itself by utilizing hardware-accelerated compute shaders to perform high-performance tensor computations directly on the user's graphics hardware. This approach allows for the execution of large language models locally, ensuring that all data processing remains private to the client d