This comprehensive course takes you deep into the world of Generative Adversarial Networks (GANs), starting from the fundamental theory and progressing to advanced state-of-the-art architectures. You will begin by understanding how GANs work, including the adversarial training process between the Generator and Discriminator, loss functions, and common training challenges such as instability and mode collapse.

The course then moves into practical implementation, where you will build your first simple GAN from scratch using PyTorch. From there, you will explore more advanced architectures including DCGAN for improved image generation quality and WGAN with Gradient Penalty for stable training.

You will also implement Conditional GANs for controlled image generation and dive into Image-to-Image Translation models such as Pix2Pix and CycleGAN, including both paper walkthroughs and full implementations. Advanced sections cover Progressive GAN (ProGAN) for high-resolution image generation and Super-Resolution models like SRGAN and ESRGAN.

By the end of this course, you will have strong practical experience implementing modern GAN architectures and a deep understanding of generative modeling techniques used in real-world AI applications.