Generative Adversarial Networks (GANs) Specialization

Generative Adversarial Networks (GANs) Specialization

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

  • Generator
  • Image-to-Image Translation
  • Glossary of Computer Graphics
  • Discriminator
  • Generative Adversarial Networks
  • Controllable Generation
  • WGANs
  • Conditional Generation
  • Components of GANs
  • DCGANs
  • Bias in GANs
  • 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.

  • 3 Courses
  • >3 months (8 hours/week)
  • Intermediate



Sharon Zhou

Sharon Zhou

Co-Founder and CEO of Lamini
Eda Zhou

Eda Zhou

Curriculum Developer
Eda Zhou completed her Bachelor’s and Master’s degrees in Computer Science from Worcester Polytechnic Institute.
Eric Zelikman

Eric Zelikman

Curriculum Engineer

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