Building Better Generative Adversary Networks (GANs)

In this course, you will: - Appreciate the challenges of GAN evaluation and compare different generative models - Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs - Identify sources of bias and how to detect it in GANs - Learn and implement techniques related to modern StyleGANs DeepLearning Specialization The Generative Adversary Networks (GANs) Specialization provides a fascinating introduction to image generation with GANs, leading the way from fundamental concepts to advanced techniques in a simple and straightforward approach. It also covers social implications, including bias in ML and how to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience with GANs. Train your own model with PyTorch, use it to generate images, and evaluate different advanced GANs. This specialization provides an accessible path for students of all levels who want to enter the field of GANs or apply GANs in their own projects, even without prior exposure to advanced mathematics and machine learning research.