Generative Adversarial Networks (GANs) are a powerful class of Machine Learning frameworks. They involve two competing Neural Networks—a generator and a discriminator—learning simultaneously through an adversarial process. This dynamic allows GANs to generate new, realistic data, from images to text, often indistinguishable from actual samples.