Diabetic Retinopathy Detection

💤This project aims to develop an automated method for detecting sleep disorders from heart rate signals.

基于 AlexNet 的花卉分类识别系统

Blindness Detection - Machine Learning Model for Diabetic Retinopathy Patients

Using laravel for creating a api route for AI(CNN) model

Recognizing hardwritten digits -- In this project we will discover the MNIST handwritten digit recognition problem and we will develop a deep learning model in Python using the Keras library that will be capable of achieving excellent results.

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

repo contains sample code snippets on #ML, #DeepLearning, #GenAI topics

A CNN-based image classifier built with TensorFlow and Keras

Tried several ML and DL models for 0-9 Digit Classification using MNIST Dataset

Detecting Brand Logos ( Mercedes, Volkswagen etc) in Images using Convolutional Neural Networks

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

30 saniyelik bir ses akışından müzik türünü CNN kullanarak sınıflandırın.

Model klasifikasi gambar menggunakan CNN untuk mengklasifikasikan gambar gestur tangan batu, gunting, dan kertas.

This project uses PyTorch to classify bone fractures. As well as fine-tuning some famous CNN architectures (like VGG 19, MobileNetV3, RegNet,...), we designed our own architecture. Additionally, we used Transformer architectures (such as Vision Transformer and Swin Transformer). This dataset is Bone Fracture Multi-Region X-ray, available on Kaggle.

An AI model that Classifies between 4 classes of Brain Tumors. Well-established CNN architecture pre-trained on a massive dataset of MRI scans. VGG16 model is used for this task.

Traditional ML with SIFT & Bag-of-Words vs. Deep Learning with CNN (VGG16 transfer learning). Explore, train, and compare techniques on a diverse face dataset. Ideal for learning image classification