MT
5.0Reviewed Dec 14, 2021
Really fun to learn. The programming assignments are good as well. They made sure I had to understand every component of different GANs. Excited for the third part
Build Better Generative Adversarial Networks (GANs)
In this course, you will: - Assess the challenges of evaluating GANs and compare different generative models - Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs - Identify sources of bias and the ways to detect it in GANs - Learn and implement the techniques associated with the state-of-the-art StyleGANs The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.
Status: Generative AI
Status: Machine Learning
IntermediateCourse29 hours
Featured reviews
AM
4.0Reviewed Nov 7, 2020
Greate course content and assignments but I want to give one feedback to the instructor. Please keep some pause while speaking. She speaks way too fast.
AS
5.0Reviewed Jan 15, 2021
Build state of the art models in a course is not an easy feat. Thanks to all the materials that have been provided.
MZ
5.0Reviewed Mar 12, 2022
This course reignited my interest in and passion about ML. I can hardly imagine the much I dont know that awaits me out there! I can barely wait for the third course!
AB
5.0Reviewed Mar 24, 2021
Great material...but the stylegan code implementation requires more video material. Instead adding one more week for ProGan part before stylegan would be helpful for the learners.
PI
5.0Reviewed Jan 30, 2021
This was a really great course, and the lectures presented really well. I learned a lot from this course.
HD
5.0Reviewed Jul 30, 2024
The course content was well-structured, making complex concepts easy to understand. Thank you for the great course.
AM
5.0Reviewed Dec 20, 2020
Name explains that it is better version than previous in terms of learning and study state of the art GANs
JM
5.0Reviewed Apr 22, 2021
Me gustaron mucho los temas en general, aunque me gustaría que en los videos hablen de las dimensiones de los tensores, a mí eso me ayudaría mucho a entender rápido
PS
4.0Reviewed Aug 29, 2023
Excellent understanding and practical experience, however the last assignment could have gone more ahead to semi final generated images
SA
5.0Reviewed Jul 18, 2021
Week 2 could explore more into GANs variants, StyleGANs or VAEs rather than focusing only on Bias theory. Overall very amazing course. Week 2 can be improved, I think.
BK
5.0Reviewed Mar 4, 2021
Good course and flexible! Quick if you want that but lots of references to the papers if you want depth.