- glossary of computer graphics
- Generator
- Image-to-Image Translation
- Generative Adversarial Networks
- Discriminator
Generative Adversarial Networks (GANs)
Completed by Milan van den Heuvel
October 30, 2020
Approximately 2 months at 10 hours a week to completeMilan van den Heuvel's account is verified. Coursera certifies their successful completion of DeepLearning.AI Generative Adversarial Networks (GANs) Specialization.
Course Certificates Completed
Build Basic Generative Adversarial Networks (GANs)
Build Better Generative Adversarial Networks (GANs)
Apply Generative Adversarial Networks (GANs)
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
Course Certificates
Earned after completing each course in the Specialization
Build Basic Generative Adversarial Networks (GANs)
DeepLearning.AI
Taught by: Sharon Zhou, Eda Zhou & Eric Zelikman
Completed by: Milan van den Heuvel by October 19, 2020
At the rate of 5 hours a week, it typically takes 4 weeks to complete this course.
Build Better Generative Adversarial Networks (GANs)
DeepLearning.AI
Taught by: Sharon Zhou, Eda Zhou & Eric Zelikman
Completed by: Milan van den Heuvel by October 23, 2020
At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.
Apply Generative Adversarial Networks (GANs)
DeepLearning.AI
Taught by: Sharon Zhou, Eda Zhou & Eric Zelikman
Completed by: Milan van den Heuvel by October 30, 2020
At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.