This Generative Adversarial Networks (GANs) course introduces one of the most important models in modern generative AI. It starts by explaining the core idea behind GANs, where two neural networks—the generator and discriminator—compete to create realistic synthetic data.
The course begins with a simple introduction to GAN concepts, helping learners understand how adversarial training works. It then guides students through building their first basic GAN from scratch to generate simple data distributions.
As the course progresses, it explores more advanced architectures such as DCGAN, which improves stability and image generation quality using convolutional networks. It also covers WGAN (Wasserstein GAN) with gradient penalty, which solves common training instability problems in GANs.
A key section introduces Conditional GANs in PyTorch, allowing controlled image generation based on input labels or conditions. The course then moves into image-to-image translation models like Pix2Pix and CycleGAN, explaining both the research papers and full implementations.
Advanced models such as ProGAN are also covered, showing how high-resolution images can be generated progressively. Finally, SRGAN (Super-Resolution GAN) is introduced, demonstrating how low-resolution images can be enhanced to high quality.
By the end of the course, learners gain a deep understanding of GAN architectures, training techniques, and real-world applications in computer vision and generative AI, making it suitable for anyone aiming to master advanced deep learning models.
