Pinned Repositories
Anomaly_detection
Anomaly Detection using Machine Learning Models on the UGR'16 Dataset. Explore the effectiveness of Isolation Forest, One-Class SVM, and XGBoost in identifying anomalies in a subsampled dataset from July 2016.
Descent
This project aims to display any function R -> R or R^2 -> R and to compare different gradient descent methods to find the minimum of this function in a visual way.
FTML_Project
MLOPS_Wine_Quality
Predicting Red Wine Quality with MLOPS: A complete system for wine quality prediction, data management, and data drift monitoring. This project uses Docker containers, Streamlit for user interfaces, PostgresSQL to save the data, and Kafka for data communication, providing a robust pipeline for wine quality predictions.
Object_detection_GPGPU
An object detector for processing videos, delivering quasi real-time results using CUDA.
PortFin
PortFin is a project designed to compare the performance of dynamically allocated investment portfolios with the market, S&P500. It leverages historical data of the assets of the S&P500 to generate new portfolios each year and allocates assets accordingly. All the information is then compiled into a PDF report.
PYBD_project
Skin_disease
This project is designed for classifying various skin diseases using the HAM10000 dataset. It leverages a trained model, explains predictions using LIME, and provides multiple interfaces for users, including a server, a graphical user interface, a command-line interface, and an API.
GPGPU_Project
DNN-centerNet
A CenterNet re-implementation focussed on multi-digit detection on the Mnist Detection Dataset
Mousteph's Repositories
Mousteph/Descent
This project aims to display any function R -> R or R^2 -> R and to compare different gradient descent methods to find the minimum of this function in a visual way.
Mousteph/MLOPS_Wine_Quality
Predicting Red Wine Quality with MLOPS: A complete system for wine quality prediction, data management, and data drift monitoring. This project uses Docker containers, Streamlit for user interfaces, PostgresSQL to save the data, and Kafka for data communication, providing a robust pipeline for wine quality predictions.
Mousteph/PortFin
PortFin is a project designed to compare the performance of dynamically allocated investment portfolios with the market, S&P500. It leverages historical data of the assets of the S&P500 to generate new portfolios each year and allocates assets accordingly. All the information is then compiled into a PDF report.
Mousteph/Anomaly_detection
Anomaly Detection using Machine Learning Models on the UGR'16 Dataset. Explore the effectiveness of Isolation Forest, One-Class SVM, and XGBoost in identifying anomalies in a subsampled dataset from July 2016.
Mousteph/FTML_Project
Mousteph/Object_detection_GPGPU
An object detector for processing videos, delivering quasi real-time results using CUDA.
Mousteph/PYBD_project
Mousteph/Skin_disease
This project is designed for classifying various skin diseases using the HAM10000 dataset. It leverages a trained model, explains predictions using LIME, and provides multiple interfaces for users, including a server, a graphical user interface, a command-line interface, and an API.