Build Better Generative Adversarial Networks (GANs)
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.
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Rating | Not enough ratings |
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Length | 4 weeks |
Effort | At the rate of 5 hours a week, it typically takes 3 weeks to complete this course. |
Starts | Jun 26 (45 weeks ago) |
Cost | $49 |
From | deeplearning.ai via Coursera |
Instructors | Sharon Zhou, Eda Zhou, Eric Zelikman |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming Data Science |
Tags | Computer Science Data Science Algorithms Machine Learning |
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Rating | Not enough ratings |
---|---|
Length | 4 weeks |
Effort | At the rate of 5 hours a week, it typically takes 3 weeks to complete this course. |
Starts | Jun 26 (45 weeks ago) |
Cost | $49 |
From | deeplearning.ai via Coursera |
Instructors | Sharon Zhou, Eda Zhou, Eric Zelikman |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming Data Science |
Tags | Computer Science Data Science Algorithms Machine Learning |
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