This tutorial introduces beginners to Generative Adversarial Networks (GANs), a revolutionary class of deep learning models used for generating realistic data such as images, audio, and text. The course begins by explaining the core concept of GANs: the adversarial relationship between the Generator, which produces synthetic data, and the Discriminator, which distinguishes real from fake data.

Learners explore the architecture of GANs and how training works using a minimax objective function. The tutorial also discusses common challenges such as mode collapse, training instability, and strategies to overcome them. Simplilearn provides practical examples to illustrate how GANs are applied in real-world tasks, including image generation, data augmentation, and creative AI projects.

By the end of this tutorial, participants will have a clear understanding of how GANs work, their components, and foundational applications in AI. This course is ideal for beginners, deep learning enthusiasts, and professionals looking to get started with generative AI technologies. It equips learners with the knowledge to explore advanced GAN architectures and practical implementations in the future.