Welcome to GAN A to Z, a collection of Jupyter notebooks and Python scripts that provide a comprehensive introduction to Generative Adversarial Networks (GANs).

• Overview
• Getting Started
• Projects
  • Project 1: Basic GAN
  • Project 2: Conditional GAN
  • Project 3: Wasserstein GAN
  • Project 4: StyleGAN
• Contributing
• License
• Acknowledgments

This repository contains a series of mini-projects that cover various aspects of GANs, from basic concepts to advanced techniques. Each project is presented as a Jupyter notebook and includes detailed explanations, code examples, and visualizations to help you understand how GANs work and how to use them.

The projects are organized in a logical order, starting with the basics of GANs and gradually building up to more advanced topics such as conditional GANs, Wasserstein GANs, and StyleGAN.

To get started, you'll need to install the dependencies listed in requirements.txt. You can do this by running:

pip install -r requirements.txt

Once you've installed the dependencies, you can run the Jupyter notebooks in the notebooks directory. Each notebook includes step-by-step instructions and code examples that you can run and experiment with.

Project 1: Basic GAN

In this project, you'll learn the basics of GANs and build a simple GAN that generates images of handwritten digits. You'll also learn how to evaluate the performance of your GAN and how to generate new images. Project 2: Conditional GAN

In this project, you'll learn how to build a conditional GAN that generates images of animals based on their species. You'll also learn how to use a pretrained classifier to guide the generation process and improve the quality of the generated images. Project 3: Wasserstein GAN

In this project, you'll learn about Wasserstein GANs, a variant of GANs that use a different loss function to train the generator and discriminator. You'll build a Wasserstein GAN that generates images of faces and compare its performance to a traditional GAN. Project 4: StyleGAN

In this project, you'll learn about StyleGAN, a state-of-the-art GAN architecture that can generate high-quality images with fine-grained control over the style and appearance. You'll build a StyleGAN that generates images of landscapes and experiment with different styles and settings.

If you find a bug or have a suggestion for a new project, please open an issue or submit a pull request. We welcome contributions from the community and are happy to help newcomers get started.

This repository is licensed under the MIT License. See the LICENSE file for more information.

We would like to thank the authors of the papers and tutorials that inspired this collection, as well as the open-source contributors who made this work possible.

If you have any questions or feedback, please feel free to reach out to us at [email protected].