Back to Build Better Generative Adversarial Networks (GANs)
Learner Reviews & Feedback for Build Better Generative Adversarial Networks (GANs) by DeepLearning.AI
683 ratings
About the Course
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.
Top reviews
UD
Nov 23, 2020
I think this course has more advanced "tricks" and models that are supported with fewer assignments, which could be one shortcoming of the course.
AB
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.
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