- glossary of computer graphics
- Generator
- Image-to-Image Translation
- Generative Adversarial Networks
- Discriminator
January 9, 2021
Approximately 2 months at 10 hours a week to completeJunghyun Park'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)
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
Earned after completing each course in the Specialization
DeepLearning.AI
Taught by: Sharon Zhou, Eda Zhou & Eric Zelikman
Completed by: Junghyun Park by December 23, 2020
At the rate of 5 hours a week, it typically takes 4 weeks to complete this course.
DeepLearning.AI
Taught by: Sharon Zhou, Eda Zhou & Eric Zelikman
Completed by: Junghyun Park by January 9, 2021
At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.
DeepLearning.AI
Taught by: Sharon Zhou, Eda Zhou & Eric Zelikman
Completed by: Junghyun Park by January 9, 2021
At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.