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

The project implements Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) using Python, focusing on the MNIST dataset. It demonstrates the ability to simulate complex neural network architectures effectively.

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Stack

Python

Architecture

The project is structured as a monolith, which allows for straightforward deployment and management. Its layered architecture enhances scalability and reliability, making it easier to maintain and extend functionalities.

Verified facts

  • The project is implemented in Python.
  • The architecture type is monolith.
  • The architecture pattern is layered.
  • The project contains GAN and VAE implementations in separate directories.
  • The project has a single repository structure.
  • The project simulates GAN and VAE networks.
  • The project is applied on the MNIST dataset.
  • The project contains 25 files.
  • The project is 100% written in Python.

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