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This course is part of Generative Adversarial Networks (GANs) Specialization
Instructors: Sharon Zhou
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73,365 already enrolled
(1,971 reviews)
Recommended experience
Intermediate level
Basic calculus, linear algebra, stats
Grasp of AI, deep learning & CNNs
Intermediate Python & experience with DL frameworks (TF / Keras / PyTorch)
(1,971 reviews)
Recommended experience
Intermediate level
Basic calculus, linear algebra, stats
Grasp of AI, deep learning & CNNs
Intermediate Python & experience with DL frameworks (TF / Keras / PyTorch)
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In this course, you will:
- Learn about GANs and their applications - Understand the intuition behind the fundamental components of GANs - Explore and implement multiple GAN architectures - Build conditional GANs capable of generating examples from determined categories 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.
See some real-world applications of GANs, learn about their fundamental components, and build your very own GAN using PyTorch!
10 videos6 readings1 programming assignment1 app item1 ungraded lab
Learn about different activation functions, batch normalization, and transposed convolutions to tune your GAN architecture and apply them to build an advanced DCGAN specifically for processing images!
9 videos5 readings1 programming assignment
Learn advanced techniques to reduce instances of GAN failure due to imbalances between the generator and discriminator! Implement a WGAN to mitigate unstable training and mode collapse using W-Loss and Lipschitz Continuity enforcement.
7 videos5 readings1 programming assignment1 ungraded lab
Understand how to effectively control your GAN, modify the features in a generated image, and build conditional GANs capable of generating examples from determined categories!
9 videos6 readings2 programming assignments1 ungraded lab
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
DeepLearning.AI is an education technology company that develops a global community of AI talent. DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.
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Reviewed on Oct 5, 2020
Great intro course, the programming assignments were pretty weak in difficulty level, could have had less hand holding there. Excited to get into more high resolution GANs soon!
Reviewed on Dec 6, 2020
A little lacking in technical knowledge. You just get to build a GAN and understand bits and pieces about why it works in very simple terms, little mathematics involved.
Reviewed on Nov 27, 2021
Great examples. Wish there were more reading material that bridged the gap between the papers (very detailed) and the slides (good for exposure to material)
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