In 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.
Build Basic Generative Adversarial Networks (GANs)
Overview
Syllabus
- Week 1: Intro to GANs
- See some real-world applications of GANs, learn about their fundamental components, and build your very own GAN using PyTorch!
- Week 2: Deep Convolutional GANs
- Learn about different activation functions, batch normalization, and transposed convolutions to tune your GAN architecture and apply them to build an advanced DCGAN specifically for processing images!
- Week 3: Wasserstein GANs with Gradient Penalty
- Learn advanced techniques to reduce instances of GAN failure due to imbalances between the generator and discriminator! Implement a WGAN to mitigate unstable training and mode collapse using W-Loss and Lipschitz Continuity enforcement.
- Week 4: Conditional GAN & Controllable Generation
- Understand how to effectively control your GAN, modify the features in a generated image, and build conditional GANs capable of generating examples from determined categories!
Taught by
Sharon Zhou
