GAN-and-VAE-networks-on-MNIST-dataset

The project implements Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) on the MNIST dataset using Python. It showcases the ability to simulate complex neural network architectures, providing valuable insights into generative modeling.

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Stack

PythonThe repository is implemented in Python.

Architecture

The system is designed as a monolith, ensuring all components are integrated within a single codebase. This architecture supports scalability and reliability, as it allows for straightforward updates and maintenance of the GAN and VAE implementations.

Verified facts

  • The repository is implemented in Python.
  • The architecture type is monolith.
  • The architecture pattern is component-based.
  • The repository contains separate directories for GAN and VAE implementations.
  • The repository contains Python files for training and utility functions.
  • The repository simulates GAN and VAE networks.
  • The GAN and VAE networks are applied on the MNIST dataset.
  • The repository contains 27 files.
  • 100% of the code is written in Python.

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