Build Better Generative Adversarial Networks (GANs)

Build Better Generative Adversarial Networks (GANs)

n this course, you will:

- Learn about GANs and their applications

- Understand the intuition behind the fundamental components of GANs

- Explore and implement multiple GAN architectures

- Build conditional GANs capable of generating examples from determined categories

The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.

Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs.

This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.

Week 1: Evaluation of GANs

Understand the challenges of evaluating GANs, learn about the advantages and disadvantages of different GAN performance measures, and implement the Fréchet Inception Distance (FID) method using embeddings to assess the accuracy of GANs!

Inception Score

Week 2: GAN Disadvantages and Bias

Learn the disadvantages of GANs when compared to other generative models, discover the pros/cons of these models—plus, learn about the many places where bias in machine learning can come from, why it’s important, and an approach to identify it in GANs!

Learn how StyleGAN improves upon previous models and implement the components and the techniques associated with StyleGAN, currently the most state-of-the-art GAN with powerful capabilities!