Generative Adversarial Networks (GANs) are a class of deep learning models used to generate synthetic data such as images, audio, and text. GANs consist of two neural networks—the generator and the discriminator—that compete against each other to improve performance. This training explains how the generator creates fake data while the discriminator evaluates its authenticity. It also covers training dynamics, loss functions, and convergence challenges in adversarial learning. You will learn how GANs are applied in image synthesis, data augmentation, deepfakes, and creative AI applications. The course also highlights best practices for training stable and effective generative models in deep learning systems.
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