Udacity Machine Learning Engineer Nanodegree Capstone Project

A simple image classifier web app built with pre-trained MobileNetV2 model. Deployed on Heroku at https://image-classifier-web-app.herokuapp.com/

Klasifikasi Sampah menggunakan Convolutional Neural Networks

GreenGuard is a multilingual AI-powered chatbot for crop disease detection, integrating AGDFNet Model. It enables farmers to upload plant images and receive real-time, text-based treatment recommendations. Using Adaptive Lesion Modules and Feature Aggregation, it enhances disease detection, helping mitigate crop losses efficiently.

Built a full-stack product identification system using TensorFlow (ResNet50, MobileNetV2, DenseNet169) for real-time, accurate predictions. Architected an optimized image processing pipeline with OpenCV, reducing latency by 40% and improving model inference efficiency.

Multilingual object detection in <6MB of neatly encapsulated javascript

React boilerplate for Tensorflow.js Image Classification model using Mobilenetv2

Chest X-Ray reports in a matter of seconds.We have created a secure Chest X-Ray image classification based website and app that can detect Covid-19, Pneumonia and Tuberculosis. Deep Learning has been used to detect the disease by using a Convolutional Neural Network(MobileNetV2) which performs classification

Web app classsificator based on the Quick, Draw! Dataset.

Handwriting Recognition using ML.NET