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Learner Reviews & Feedback for Apply Generative Adversarial Networks (GANs) by DeepLearning.AI

4.8
stars
546 ratings

About the Course

In this course, you will: - Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity - Leverage the image-to-image translation framework and identify applications to modalities beyond images - Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa) - Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures - Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one 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

PI

Jan 30, 2021

This was a really great course, and the lectures presented really well. I learned a lot from this course.

LG

Feb 18, 2021

Great to put the GANs to practice and see what you can achieve. This was the icing on the cake for me. Thanks Sharon for your clear explanations!

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101 - 101 of 101 Reviews for Apply Generative Adversarial Networks (GANs)

By Farid A

•

Feb 7, 2024

The instructor did not explain things very deeply! I was expecting more technical lectures on how to deep dive into the topic rather than just providing high level overview of the GANs variants. Some optional notebooks definitely worth to be part of the lectures for more motivations, such as pix2pixHD probably as one lecture. The specialization could potentially divided into several more courses with more detailed and technical lectures. The workflow was was not easy to follow as in the level of 2nd and 3rd courses are extremely higher than course 1 which was mainly supposed to provided overview of the GAN and inspire a potential learner to continue further.