Metadata-Version: 2.1
Name: gans_implementations
Version: 0.0.2
Summary: A bunch of GAN implementations
Home-page: https://github.com/UdbhavPrasad072300/GANs-Implementations
Author: Udbhav Prasad
Author-email: udbhavprasad072300@gmail.com
License: MIT
Description: # GANs-Implementations
        
        GANs Implementations and their Training (in .ipynb)
        
        Implemented:
        <ul>
          <li>Vanilla GAN</li>
          <li>DCGAN - Deep Convolutional GAN</li>
          <li>WGAN - Wasserstein GAN</li>
          <li>SNGAN - Spectrally Normalized GAN </li>
          <li>StyleGAN</li>
        </ul>
        
        ## Installation
        
        <a href="https://pypi.org/project/gans-implementations/">PyPi Installation</a>
        
        ```bash
        $ pip install gans-implementations
        ```
        
        ## Handwritten Digits - MNIST 
        
        <ul>
          <li><a href="https://github.com/UdbhavPrasad072300/GANs-Implementations/blob/main/notebooks/GAN%20with%20BCE%20-%20MNIST.ipynb">GAN with BCELoss</a></li>
          <li><a href="https://github.com/UdbhavPrasad072300/GANs-Implementations/blob/main/notebooks/DCGAN%20with%20BCE%20-%20MNIST.ipynb">DCGAN with BCELoss</a></li>
          <li><a href="https://github.com/UdbhavPrasad072300/GANs-Implementations/blob/main/notebooks/SN-WGAN%20with%20GP%20-%20MNIST.ipynb">SN-WGAN with Wasserstein Loss</a></li>
        </ul>
        
        ## Work Cited
        
        https://www.coursera.org/specializations/generative-adversarial-networks-gans?
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3 :: Only
Description-Content-Type: text/markdown
