Building Auto-encoders using Deep Learning models in PyTorch
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Updated
Jun 15, 2022 - Jupyter Notebook
Building Auto-encoders using Deep Learning models in PyTorch
Implementation of some famous machine learning algorithm from scratch
Implemented K-Means Clustering from scratch on Cifar-10 dataset, Optimized the model using Auto- Encoders.
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)
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)
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