Generative Adversarial Networks, or GANs, are a type of machine learning algorithm that involves two competing AI models: the generator and the discriminator. Unlike traditional supervised learning models that make predictions based on input data, GANs function through unsupervised learning, where the generator creates fake samples while the discriminator evaluates them. The generator's objective is to produce samples so convincing that they can deceive the discriminator, which must determine whether a sample is real or generated. This interplay is particularly effective in applications like image generation. For instance, to train a GAN to create images of flowers, the discriminator first learns to identify real flower images by analyzing their features such as color, shape, and shading. As it improves, the generator begins to create artificial flowers, iterating through cycles of refinement until the generated samples are indistinguishable from real ones. The process emphasizes the essential adversarial relationship between the two models, driving both the generator's creativity and the discriminator's accuracy.