This is a web demo for camera-based PPG sensing (rPPG).

☕️ A vscode extension for netron, support *.pdmodel, *.nb, *.onnx, *.pb, *.h5, *.tflite, *.pth, *.pt, *.mnn, *.param, etc.

VSCode Extension of Type4Py

(computer vision in hosipitals) this is a simple rest api that classifies pneumonia infection weather it is Normal, Pneumonia Virus or Pneumonia Bacteria from a chest-x-ray image.

Digit recognition with dl4j

code generation chatbot for stabilty AIs stablecode

AI Image generator and NFT Creator

A library simplifying the work with TensorFlow.js in Node.

ml5.js example for ReactJS with Typescript variant. This repository contains two examples, one of pose-net and another of face-api. Read about this in my blog (https://analyticsindiamag.com/how-to-teach-machines-inside-a-browser/).

Deep learning framework for TypeScript

A simple implementation of a Multi-Layer Perceptron (MLP) neural network in TypeScript.使用 TypeScript 实现的简单多层感知器（MLP）神经网络。

AI-powered tool for early detection of nail-related diseases from images. Upload a nail photo and get real-time predictions using a high-accuracy deep learning model (97.8% accuracy). Built for scalability, efficiency, and healthcare accessibility.

Core TypeScript library for accessing the UFDL backend, managing the communication.

Spam comment detection on social media using emoji analysis and post-comment pair context to improve classification accuracy.

Art Guard is an AI-driven solution designed to authenticate and protect artwork, ensuring the integrity of each piece by identifying whether it is real, handmade, or AI-generated.

Modern web interface for Stable Diffusion 3 text-to-image generation. Features real-time progress tracking, local storage, and a sleek dark mode UI. Built with Next.js 15, FastAPI, and TorchServe.

Este projeto fornece a interface frontend responsável por interagir com a API de reconhecimento de dígitos manuscritos, permitindo ao usuário desenhar e visualizar os resultados da classificação. A aplicação integra o modelo de Deep Learning treinado com Keras/TensorFlow, utilizando o dataset público MNIST.