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Back to Apply Generative Adversarial Networks (GANs)

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

MS

Oct 30, 2020

great course and great material really, keep the great work and hopefully seeing more of your courses again Zho <3

JC

Jan 16, 2021

It is a great course that you need to take time to understand fully, particularly the optional materials and readings are super valuable to extend understanding.

Filter by:

51 - 75 of 101 Reviews for Apply Generative Adversarial Networks (GANs)

By Serge T

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Nov 18, 2020

Great course and a fantastic Specialisation! Would recommend to everyone interested!

By Antoreep J

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Apr 24, 2021

Course 3 was better than Course 2. Course 2's assignments were bit confusing.

By Matthew B E R

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Nov 28, 2020

A wonderful course, which serves as a great conclusion to the specialization.

By Asaad M A A

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Sep 13, 2021

I really enjoyed taking this course. I want to thank all the instructors.

By Hoda F

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Sep 8, 2022

I really enjoyed the course!

I hope you add new matrial to the course.

By Charlie B J

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Nov 26, 2021

Incredible course. Thorough yet understandable for anyone interested

By Paritosh B

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Dec 5, 2020

Great content. Thanks a lot for creating this wonderful course. :)

By Rohan H J

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Aug 3, 2021

Very detailed study. A must learn for people working with GANs

By Shivender K

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Jan 24, 2021

Very complex specialization but significantly helpful

By Samuel H K

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Mar 4, 2021

Awesome course! Direct application to my research!

By nghia d

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Dec 21, 2020

amazing course! thanks coursea, thanks Instructors

By Evgenii T

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Jan 31, 2021

Easy yet fundamental enough for an eager learner.

By Shams A

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Jul 23, 2021

Amazing course. Thanks so much for offering it!

By Ali G

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Jul 22, 2021

Very informative and easy-to-understand!

By Gokulakannan S

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Dec 26, 2020

Nice course enjoyed it a lot. Thanks!

By James H

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Nov 17, 2020

Very thorough and clearly explained.

By Xiaoyu X

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Aug 1, 2021

Very good lectures and assignments!

By Emmanuelle S

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Jun 29, 2023

Excellent conclusion to the series

By Kenneth N

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Jun 27, 2022

exceptional and clear instructions

By Parma R R

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May 10, 2023

Very good and well design course!

By Jesus A

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Nov 22, 2020

Great applications cases of GANs

By Linjun Y

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Aug 17, 2022

Great course for everyone!

By Dela C F S (

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Jun 6, 2021

Full of amazing content! :D

By Manuel R

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Mar 30, 2021

It was a nice experience!

By amadou d

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Mar 11, 2021

Excellent! Thank You all!