Web component for removing image backgrounds in the browser

Cat & Dog Classifier is a browser-based machine learning application that classifies uploaded images as either cat or dog using a convolutional neural network. The project demonstrates how deep learning models can be integrated into a modern frontend stack using React, TypeScript, and TensorFlow.js, enabling real-time inference directly in the brow

Neural Networks can learn (almost!) anything.

AI-powered image upscaling and enhancement in the browser, built with React, TypeScript, and TensorFlow.js (ESRGAN & MAXIM models).

An AI-powered, vibe-aware location discovery app combining ML, LLMs, and Maps.

In-browser neural network playground — train CNNs and dense nets on MNIST, Fashion-MNIST, and CIFAR-10 in real time

Innovative AI-powered music player built with Next.js 14. Integrates TensorFlow.js for real-time, browser-based hand gesture controls and the Spotify Web API for seamless audio streaming, backed by a robust NextAuth and MongoDB architecture.

Interactive machine learning demos with TensorFlow.js featuring linear regression and binary classification with Materialize CSS framework

Browser-based audio similarity search — no server, no uploads, runs entirely in your browser.

Enterprise-grade NLU bot trainer with 5-classifier stacking ensemble (171K params) — runs entirely in the browser

AI-powered fence detection & recoloring tool using UNet++ deep learning. Upload fence photos, apply realistic stain colors instantly.

North Korean military activity risk analysis — fine-tuned LFM2-350M running in-browser via WebGPU

Benchmarking fine-tuned transformer models for multilingual sentiment analysis (EN/AR/RU) running entirely in the browser.

In-browser academic paper search engine with semantic search, knowledge graph visualization, and multi-source support (arXiv, OpenAlex, Europe PMC).