This course focuses on generative adversarial networks (GANs), which have reshaped machine learning and deep learning landscapes. This course also aims to provide a comprehensive introduction to GANs, starting with a deep dive into deep learning and generative models and their extensive applications in artificial intelligence.

You will start by getting an overview of deep learning and generative models. You will then explore the wide-ranging applications where GANs can be utilized. You will get hands-on experience in constructing, evaluating, and enhancing GANs. You’ll also learn how to train GANs for high-resolution image generation, implement conditional GANs for output control, and adopt progressive training methods to refine GAN performance.

After completing this course, you will have a good understanding of the latest advancements within the GAN frameworks used in computer vision, natural language processing (NLP), and audio processing.