- Generator
- Image-to-Image Translation
- glossary of computer graphics
- Discriminator
- Generative Adversarial Networks
Generative Adversarial Networks (GANs)
Completed by Athanasios Charisoudis
November 29, 2020
Approximately 10 weeks to complete.Athanasios Charisoudis'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: Athanasios Charisoudis by November 2, 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: Athanasios Charisoudis by November 16, 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: Athanasios Charisoudis by November 29, 2020
At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.
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