Developed a Generative Adversarial Network (GAN) using Python and TensorFlow to generate images resembling some great artworks

Built and trained a generator model to create images from random noise and a discriminator model to distinguish between real and generated images.

Employed Binary Cross-Entropy Loss and Adam optimizers to enhance model performance.

Implemented a training loop to alternately train the generator and discriminator over multiple epochs