manos's repositories
metacovid-siamese-neural-network
MetaCovid diagnosis using Siamese Neural Networks and few-shots learning
radiomics-ml-ga
Radiomics machine learning classification enchanced with genetic algorithms
intro-to-ml-tensorflow
Projects and exercises for the Udacity Intro to Machine Learning with TensorFlow course
radiomics-features-extraction
The purpose of this project is to automate the radiomics workflow and find the best composition of features, settings and parameters that can be used in order to achieve the best performance. Performance can be measured with some specific metrics (like accuracy or AUC) after our AI model has been trained.
ML_SageMaker_Studies
Case studies, examples, and exercises for learning to deploy ML models using AWS SageMaker.
covid-diagnosis-CNN
In this project, we present a deep learning-based approach to detect COVID-19 infections from CXR images. We propose a deep CNN model to classify a COVID-19. To put it in another way, we built a model that can identify COVID-19 infections from CXR images.
deep-learning
Repo for the Deep Learning Nanodegree Foundations program.
dog-breed-classifier-CNN
Dog breed classifier developed with CNN and transfer learning using Pytorch - Udacity Capstone Project
DSND_Term2
Contains files related to content and project of DSND Term 2
GARI
GARI (Genetic Algorithm for Reproducing Images) reproduces a single image using Genetic Algorithm (GA) by evolving pixel values.
my-jekyll-website
Learning jekyll
NeuralGenetic
Building and training artificial neural networks (regression or classification) using the genetic algorithm.
opencv-course
Learn OpenCV in 4 Hours - Code used in my Python and OpenCV course on freeCodeCamp.
pyradiomics
Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks.
python_for_microscopists
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
restful-prototype-asset-management
This project is created for a job application and it represents my skills in full-stack web development. The frontend is implemented with Angular and TypeScript The Backend is implemented with Java Spring framework and Hibernate
sagemaker-deployment
Code and associated files for the deploying ML models within AWS SageMaker
scikit-learn
scikit-learn: machine learning in Python