Generative Adversarial Networks (GANs) Specialization

Specialization: Gain practical knowledge of how generative models work. Construct and design your own generative adversarial model. Analyze how generative models are being applied in various commercial and exploratory applications.

Course 1: In this course, you will understand the fundamental components of GANs, build a basic GAN using PyTorch, use convolutional layers to build advanced DCGANs that processes images, apply W-Loss function to solve the vanishing gradient problem, and learn how to effectively control your GANs and build conditional GANs.

Course 2: In this course, you will understand the challenges of evaluating GANs, 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, and learn and implement the techniques associated with the state-of-the-art StyleGAN.

Course 3: In this course, you will use GANs for data augmentation and privacy preservation, survey more applications of GANs, and build Pix2Pix and CycleGAN for image translation.

This Specialization is for software engineers, students, and researchers from any field, who are interested in machine learning and want to understand how GANs work.

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.

Learners should have a working knowledge of AI, deep learning, and convolutional neural networks. They should have intermediate Python skills as well as some experience with any deep learning framework (TensorFlow, Keras, or PyTorch). Learners should be proficient in basic calculus, linear algebra, and statistics.

We highly recommend that you complete the Deep Learning Specialization prior to starting the GANs Specialization.

After completing this Specialization, you will have learned how to achieve the state-of-the-art in realistic generation. You will be able to generate realistic images, edit those images by controlling the output in a number of ways (eg. convert a horse to a zebra or lengthen your hair or make yourself older), quantitatively compare generators, convert an image to another (eg. turning a sketch into a photo-realistic version), animate still images, solve many of the challenges that GANs are notorious for, and more.

This Specialization was created by Sharon Zhou, a CS PhD candidate at Stanford University, advised by Andrew Ng. Sharon Zhou’s work in AI spans from theoretical to applied, in medicine, climate, and more broadly, social good. Previously a machine learning product manager at Google and various startups, Sharon is a Harvard graduate in CS and Classics. She likes humans more than AI, though GANs occupy a special place in her heart.

This is a Specialization made up of 3 courses.

We recommend taking the courses in the prescribed order for a logical and thorough learning experience.

This specialization consists of three courses. At the rate of 5 hours a week, it typically takes 4  weeks to complete each course.

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

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.

No, you cannot take this course for free. 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. If you cannot afford the fee, you can apply for financial aid.