Introduction to GANs: Image Generation Concepts and Self-Assessments

Generative Adversarial Networks (GANs) have revolutionized how we generate realistic digital imagery, but grasping how the generator and discriminator interact can be challenging. This course guides you through the foundational architectures, training dynamics, and evaluation methods of GANs, helping you build a solid conceptual framework. What you'll learn: Understand the dual-network architecture of generators and discriminators; Analyze training dynamics, loss functions, and common failure modes like mode collapse; Explore latent space manipulation and modern GAN developments; Evaluate generated images using modern metrics like Frechet Inception Distance (FID); Practice your knowledge with targeted conceptual quizzes and written code-walkthroughs. You will start with basic generative concepts and foundational definitions before moving into adversarial training loops. This course is designed for beginners in machine learning and computer vision, requiring no prior experience with generative models. Begin exploring the fascinating world of generative AI today.