Generative Adversarial Networks (GANs) Specialization
Break into the GANs space. Master cutting-edge GANs techniques through three hands-on courses!
Offered By


What you will learn
Understand GAN components, build basic GANs using PyTorch and advanced DCGANs using convolutional layers, control your GAN and build conditional GAN
Compare generative models, use FID method to assess GAN fidelity and diversity, learn to detect bias in GAN, and implement StyleGAN techniques
Use GANs for data augmentation and privacy preservation, survey GANs applications, and examine and build Pix2Pix and CycleGAN for image translation
Skills you will gain
About this Specialization
Applied Learning Project
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.
- Basic calculus, linear algebra, stats
- Grasp of AI, deep learning & CNNs
- Intermediate Python & experience with DL frameworks (TF / Keras / PyTorch)
- Basic calculus, linear algebra, stats
- Grasp of AI, deep learning & CNNs
- Intermediate Python & experience with DL frameworks (TF / Keras / PyTorch)
Offered by

DeepLearning.AI
Founded by Andrew Ng, DeepLearning.AI is an education technology company that develops a global community of AI talent.
Frequently Asked Questions
What is the refund policy?
Can I just enroll in a single course?
Is financial aid available?
Can I take the course for free?
Is this course really 100% online? Do I need to attend any classes in person?
Will I earn university credit for completing the Specialization?
What are GANs?
What are the applications of GANs?
Why are GANs important?
What is the GANs Specialization about?
What will I learn in the GANs Specialization?
Who is the GANs Specialization for?
What background knowledge is necessary?
What will I be able to do upon completing the Specialization?
Who created the GANs Specialization?
Is this a standalone course or a Specialization?
Do I need to take the courses in a specific order?
Can I audit the Specialization?
How long does it take to complete the Specialization?
More questions? Visit the Learner Help Center.