Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs.
Rooted in game theory, GANs have wide-spread application: from improving cybersecurity by fighting against adversarial attacks and anonymizing data to preserve privacy to generating state-of-the-art images, colorizing black and white images, increasing image resolution, creating avatars, turning 2D images to 3D, and more.
About this Specialization
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 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.
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
In this course, you will: explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity; leverage the image-to-image translation framework and identify applications to modalities beyond images; implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa); compare [...]
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 [...]
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.