Building Auto-encoders using Deep Learning models in PyTorch

Implementation of some famous machine learning algorithm from scratch

This repository if for creating auto-encoders easily. The main focus of the auto-encoders on this page is for genetic and spectral data analysis but likely could be used for any high dimensional data

A docker environment and notebooks to experiment with the extraction of moore machines from RNN RL policies

Various Deep Learning Projects (2022- 2023)

Implemented K-Means Clustering from scratch on Cifar-10 dataset, Optimized the model using Auto- Encoders.

Indoor Human Walking Path Reconstruction from a FMWC Radar Signal

PyTorch implementation for the framework presented in the paper: Generative Fourier-based Auto-Encoders: Preliminary Results paper

Basil is a master detective, excellent at spotting inconsistencies and uncovering hidden clues. Think Anamoly Detection.

Research on Material Science using Neural Networks black box approach

Multiple Sclerosis Detection using 3D Auto-Encoders for volumetric MRI scans

An age regression/progression application using GANS & CAE

Collaborative and hybrid recommendation systems

Explore Network Anomaly Detection Project 📊💻. It achieves an exceptional 99.7% accuracy through a blend of supervised and unsupervised learning, extensive feature selection, and model experimentation. Stunning data visualizations using synthetic network traffic data offer insightful representations of anomalies, enhancing network security.

A module of Fraud detection in Credit card applications

In this repository, I'm going to include some well documented projects, that I've implemented during my learning and I will keep it updated

simple VAE pytorch implementation

Master thesis: Structured Auto-Encoder with application to Music Genre Recognition (results)