Detection of Diabetic Retinopathy using a Fully Connected Neural Network for Data Processing (Tensorflow 1.12.0 and Python 3.6.6)

Classification of 3 species of flowers (versicolor, virginica, setosa) belonging to the Iris family, using a Fully Connected Neural Network for Data Processing (Tensorflow 1.12.0 and Python 3.6.6)

Neural networks on digit recognition. As part of the MITx course on machine learning with Python - from linear models to deep learning

Fully Convolutional Neural Network for heart segmentation

This a simple C++ fully connected neural network class for Course COMP3046.

Implementations of components of Neural Networks from scratch in Numpy

A ROS1 self-driving car for the "Autonomous Driving Competition". The vehicle itself is a DonkeyCar with RaspberryPi and Raspberry Pi Camera.

기초딥러닝 Term Project [TO IMPROVE]

Enjoy the major Deep Learning Projects !!!

Building fully connected neural network from zero without using deep learning libraries such as Pytorch.

Train FCNN to label each pixel of driving footage as being part of the road (or not). For use in perception systems of autonomous cars.

A collection of Deep Learning labs for the INSAT Data Science course.

This project is about training a deep neural network to identify and track a target in simulation using Udacity's RoboND drone simulator. 🛸 Applications like this are key to many fields of robotics and the techniques applied can be extended to scenarios like advanced cruise control in autonomous vehicles or human-robot collaboration. 👨‍🏫

Developed a CNN model to classify skin moles as benign or malignant using a balanced dataset from Kaggle, achieving a test accuracy of 81.82% and an AUC of 89.06%. Implemented data preprocessing by resizing images to 224x224 pixels and normalizing pixel values, enhancing model performance and stability.

Ford Otosan Internship Project 2020 || Freespace Segmentation.

Perform semantic segmentation of photo captured of the front view of a driver identifying the road surface. It's for the purpose of self-driving-car. It's performed by full convolution neural network.

This project demonstrates a complete pipeline for recognizing handwritten digits using the MNIST dataset. The project is implemented in Python using Jupyter Notebook, and it covers data loading, preprocessing, model training, and performance evaluation of a Fully Connected Neural Network (FCNN).

An implementation of the iris flower classification using Keras on the iris dataset

Concise neural network with C++ and CUDA