This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.

This code implements the EEG Net deep learning model using PyTorch. The EEG Net model is based on the research paper titled "EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer Interfaces".

Implementation of Research Paper "Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation"

A novel architecture for enhancing image classification. Reference paper: https://arxiv.org/abs/2104.12294

This educational repository focuses on working with three types of medical data: tabular data, ECG and EEG signals. It provides implementations of machine learning and deep learning models for processing and analyzing these medical data, with practical projects based on recent research articles.

Smart Automation Controller for Precision Agriculture

Implementation and research paper of MobileNet

Gravepine disease detection with Deep Learning and Transfer Learning.