Lesson 1
Introduction to Generative Adversarial Networks
Introduction to this course, prerequisites, and your course instructor.
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.
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
4 weeks
Real-world Projects
Completion Certificate
Last Updated January 22, 2024
Generative adversarial networks • Model evaluation • Deep learning techniques • Markov games
Jupyter notebooks • PyTorch • Basic calculus
Course Lessons
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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