This project explores Generative Adversarial Networks (GANs) to generate realistic fake human faces. GANs are unsupervised generative models that learn from input data and create new samples resembling the original distribution. The GAN architecture consists of a Generator network that generates images and a Discriminator network that distinguishes real from fake. The project involves importing necessary libraries loading and visualizing a face image dataset defining and training the Generator and Discriminator models and evaluating their performance. The trained models are used to generate batches of fake face images which are then saved and made available for download. The project showcases the capabilities of GANs in generating synthetic images and demonstrates proficiency in deep learning image processing and data generation techniques.
