Building Generative Adversarial Networks

Course

Learn to understand and implement a Deep Convolutional GAN (generative adversarial network) to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs.

Intermediate

3 weeks

Real-world Projects

Completion Certificate

Last Updated July 25, 2024

Skills you'll learn:

Generative adversarial networks • Model evaluation • Deep learning techniques • Markov games

Prerequisites:

Jupyter notebooks • PyTorch • Basic calculus

Course Lessons

Lesson 1

Introduction to Generative Adversarial Networks

Introduction to this course, prerequisites, and your course instructor.

Lesson 2

Generative Adversarial Networks

Ian Goodfellow, the inventor of GANs, introduces you to these exciting models. You'll also implement your own GAN on the MNIST dataset.

Lesson 3

Training a Deep Convolutional GANs

In this lesson, you'll implement a Deep Convolution GAN to generate complex color images.

Lesson 4

Image to Image Translation

Jun-Yan Zhu, one of the creators of the CycleGAN, will lead you through Pix2Pix and CycleGAN formulations that learn to do image-to-image translation tasks.

Lesson 5

Modern GANs

In this lesson, you will implement more advanced GAN architectural techniques that have had a significant impact on the realism of generated images.

Lesson 6 • Project

Face Generation

Define two adversarial networks, a generator, and a discriminator, and train them until you can generate realistic faces.

Taught By The Best

Thomas Hossler

Sr Deep Learning Engineer

Thomas is originally a geophysicist but his passion for Computer Vision led him to become a Deep Learning engineer at various startups. By creating online courses, he is hoping to make education more accessible. When he is not coding, Thomas can be found in the mountains skiing or climbing.

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