Generative Adversarial Networks (GANs) Specialization

3 Courses

Intermediate

3 months (8 hours/week)

Sharon Zhou, Eda Zhou, Eric Zelikman

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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

Enroll Now

Syllabus

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.

Week 1: Intro to GANs

Learn about GANs and their applications, understand the intuition behind the basic components of GANs, and build your very own GAN using PyTorch.

Week 2: Deep Convolutional GAN

Build a more sophisticated GAN using convolutional layers. Learn about useful activation functions, batch normalization, and transposed convolutions to tune your GAN architecture and apply them to build an advanced DCGAN specifically for processing images.

Week 3: Wasserstein GANs with Normalization

Reduce instances of GANs failure due to imbalances between the generator and discriminator by learning advanced techniques such as WGANs to mitigate unstable training and mode collapse with a W-Loss and an understanding of Lipschitz Continuity.

Week 4: Conditional and Controllable GANs

Understand how to effectively control your GAN, modify the features in a generated image, and build conditional GANs capable of generating examples from determined categories.

Instructors

Sharon Zhou

Instructor

Co-Founder and CEO of Lamini

Eda Zhou

Curriculum Developer

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

Generative Adversarial Networks (GANs) Specialization

What you will learn

Skills you will gain

Syllabus

Week 1: Intro to GANs

Week 2: Deep Convolutional GAN

Week 3: Wasserstein GANs with Normalization

Week 4: Conditional and Controllable GANs

Week 1: GAN Evaluation

Week 2: GAN Disadvantages and Bias

Week 3: StyleGAN and Advancements

Week 1: GANs for Data Augmentation and Privacy Preservation

Week 2: Image-to-Image Translation

Week 3: Image-to-Image Unpaired Translation

Course 1: Build Basic Generative Adversarial Networks (GANs)

Course 2: Build Better Generative Adversarial Networks (GANs)

Course 3: Apply Generative Adversarial Networks (GANs)

Instructors

Sharon Zhou

Eda Zhou

Eric Zelikman

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