Deep Learning with PyTorch: Build an AutoEncoder

This repository contains materials and code for building an AutoEncoder using PyTorch. It includes a complete Jupyter Notebook implementation, helper utilities, and illustrative images for understanding encoder--decoder architecture and denoising AutoEncoders.

📁 Repository Structure

Deep-Learning-with-PyTorch-Build-an-AutoEncoder-main/
│
├── README.md
└── Project-files/
    ├── Build an AutoEncoder.ipynb
    ├── helper.py
    ├── dataset.png
    └── denoising_autoencoder.png

📄 Contents

1. Build an AutoEncoder.ipynb

A fully implemented notebook demonstrating: - Construction of encoder and decoder networks\

• Training a basic AutoEncoder\
• Visualizing reconstructions\
• Extending the model to a denoising AutoEncoder

2. helper.py

Contains utility functions used within the notebook, such as data loading, plotting, and preprocessing helpers.

3. Images

• dataset.png --- Sample dataset visualization.\
• denoising_autoencoder.png --- Diagram illustrating the denoising AutoEncoder architecture.

🚀 Getting Started

Requirements

• Python 3.8+
• PyTorch\
• NumPy\
• Matplotlib\
• Jupyter Notebook

Install dependencies:

pip install torch numpy matplotlib jupyter

Running the Notebook

jupyter notebook "Build an AutoEncoder.ipynb"

📬 Contact

If you need help modifying the AutoEncoder, extending it to convolutional layers, or applying it to your own dataset, feel free to ask!