Udacity-SelfDrivingCar-ND-CarND-Traffic-Sign-Classifier-Project

Behavioral Cloning: Navigating a Car in a Simulator

image prep and augmentation for object detection

project 1 (AI in health Care): A Convolutional Neural Network to classify chest xray images of a person.

This repository contains code related to hackerearth deep learning challenge - Identify Dance form

Final group project of "Human Data Analytics" course at University of Padova

The aim of this project is to use the LiDAR sensor of the new Apple devices in combination with the camera sensors in order to classify cars (based on the make and model) using deep learning. This repository contains the corresponding code consisting of the iOS App, a preprocessing and augmentation script, and our custom neural network.

The project develops a lung disease classification model using ResNet-50 on a Jetson Nano Board, achieving high accuracy in detecting Pneumonia, Tuberculosis, Cancer, and COVID-19 from chest X-rays.

Data augmentation for TensorFlow

Data augmentation for NLP, presented at EMNLP 2019

Comparing YOLO and MixNet architectures for image-based human detection

Demo of data augmentation: a technique to increase the diversity of your training set by applying random (but realistic) transformations, such as image rotation

The introduction to deep learning practices.

Source Code, data, and results for my paper titled Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chinese Question Matching.

Code for my Bachelor's thesis "Drone image and video encoding and processing using machine learning"

Ingredient recognition and recipe suggestions

Estimating the age from images while tacking the bias with respect to the protected attributes (Age, Gender, Ethnicity, Face Expression)

In this project, I created a multiclass classification CNN-based model which can detect melanoma with a validation accuracy of 80%. The dataset consists of 2357 images of malignant and benign oncological diseases.

This repository contains a Generative Adversarial Network (GAN) implementation for generating synthetic breast cancer images using PyTorch. The GAN is trained on a dataset of breast cancer images.