Variational Autoencoder Implementation

This repository contains an implementation of a Variational Autoencoder (VAE) using Python and PyTorch. The project explores how VAEs can be used for dimensionality reduction, data generation, and representation learning.

Project Files

• variational-autoencoder-implementation.ipynb: The Jupyter Notebook with the code for training and evaluating the VAE.
• VAE_project_report.pdf: A detailed report explaining the theory behind Variational Autoencoders, the approach used, results, and conclusions.

Overview

• The notebook includes data preprocessing, model training, and results visualization.
• The report provides an in-depth explanation of the methodology, including loss functions like the Kullback-Leibler Divergence and Reconstruction Loss.

How to Run

1. Clone this repository:

   git clone https://github.com/NeginHeidarifard/variational-autoencoder-implementation.git