This video explains Generative Adversarial Networks (GANs). GANs learn to generate data by pitting two neural networks against each other: a generator creating fake data and a discriminator identifying real vs. fake. The generator learns to fool the discriminator, resulting in realistic data. Challenges include gradient vanishing, addressed by modifying the loss function. Recent advancements significantly improved GAN performance, generating high-quality images.