Build Better Generative Adversarial Networks (GANs)

In this course, you will:- Assess the challenges of evaluating GANs 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 the ways to detect it in GANs
• Learn and implement the techniques associated with the state-of-the-art StyleGANs

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.

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Syllabus

Syllabus - What you will learn from this course
Week 1
Week 1: Evaluation of GANs
Week 2
Week 2: GAN Disadvantages and Bias
Week 3
Week 3: StyleGAN and Advancements

FAQ

When will I have access to the lectures and assignments?

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Reviews

This course reignited my interest in and passion about ML. I can hardly imagine the much I dont know that awaits me out there! I can barely wait for the third course!

Assignments for me where the key to have proper understanding. The assignments are great where the difficulty gradually increases.

Really fun to learn. The programming assignments are good as well. They made sure I had to understand every component of different GANs. Excited for the third part

Both course 1 and course 2 of this specialization are excellent and programming assignments as well.