Diabetic Retinopathy Detection

基于 AlexNet 的花卉分类识别系统

CNN-based classifiers for classifying audio samples into 13 distinct categories, such as laughter, car horn, dog bark, etc.

This project utilizes deep learning to detect pneumonia from chest X-ray images, offering both model training and real-time inference through Jupyter Notebooks and a Flask web application. With a focus on flexibility and user-friendliness, it empowers users to fine-tune model parameters and seamlessly deploy the trained model for accurate pneumonia

College project | Number recognition Kaggle challenge with dataset MNIST 🆒 😄 🐍 ✔️

30 saniyelik bir ses akışından müzik türünü CNN kullanarak sınıflandırın.

This project uses TensorFlow to implement a Convolutional Neural Network (CNN) for image classification. The goal is to classify skin lesion images into different categories. The dataset used is HAM10000, which contains skin lesion images with associated metadata. The actual accuracy of the model is 90%. 🚀🚀

Multilayer Authenticity Identifier (MAI), a CNN model that attempts to identify synthetic AI images.

Repo for Implementing Research Papers & Projects related to Machine Learning

Hong Kong Polytechnic Unversity Master degree's AI Concept (COMP5511) Course project

Simple ML API built on FastApi for classifying breed of dog based on uploaded image with provided token authorization. PyTorch CNN model trained on Stanford Dogs dataset

A demo project for MNIST CNN with PyTorch

sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data

A deep learning model to classify between dogs and cats using Keras

hyper-sinh: An Accurate and Reliable Activation Function from Shallow to Deep Learning in TensorFlow, Keras, and PyTorch

This repository contains a kidney disease classification model implemented using deep learning techniques, particularly a Convolutional Neural Network (CNN) classifier. The model is trained to classify kidney disease based on medical images.

In this project you can use webcam or you can upload images to detect face and identify emotions.

rapid, precise tempo prediction in python

Using Few Shot Learning (FSL) for image classification on Oxford 17 Flowers dataset. Part of HKU COMP3340 Group 10 Project (2023-24 Sem2).