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

1,009 ratings
255 reviews

About the Course

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....

Top reviews

Oct 10, 2020

great course, only teaching what's needed, doesn't push you a lot in the coding assignments, as much as it requires you much more work to understand the codes and the science behind it.

Oct 6, 2020

Excellent course. The videos were a pleasure to watch, the assignments were clear and allowed you to go as shallow or as in depth as you desired, and the mentors were very helpful.

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176 - 200 of 257 Reviews for Build Basic Generative Adversarial Networks (GANs)

By makoto n

Oct 18, 2020


By Shoumik S C

Nov 1, 2020

Sharon is a great teacher!

By Cường N N

Dec 5, 2020

a Very interesting course

By ahmed h

Dec 30, 2020

very interesting course

By amadou d

Mar 7, 2021

Excellent. Thank YOU!!

By Natasha D

Oct 30, 2020

Great course on GANs!

By Duy Đ

Dec 29, 2020

Very helpfull for me

By Ji Y R

Jan 22, 2021

Really instructive!

By Kshitiz R

Oct 24, 2020

Excellent Course!!!

By Luis M C S

Nov 9, 2020

hard but worth it

By Josue D G P

Mar 29, 2021

Excelente curso.

By Robin S

Dec 20, 2020

Truly brilliant

By Md A R

Feb 23, 2021

Amazing Course

By Wildson B B L

Jan 2, 2021

Great course!

By Victor d l C

Oct 11, 2020


By Irving G B P

Apr 17, 2021

Amazing work

By Lâm Đ A

Jan 5, 2021

Good course

By Andrew C

Nov 24, 2020

Loved it.

By Hoang C V

Feb 1, 2021

great !!

By Maciej A

Oct 11, 2020


By Nancy A A

Dec 24, 2020


By jiangli

Jan 11, 2021


By Martin J

Nov 23, 2020

An excellent course to bring one up to speed with current developments in GANs. Quite a bit of reading around the subject, in addition to the references provided, is necessary, particularly if you are new to using pytorch or python. But the accompanying Slack support is a life line.

I think this course is even more effective if you have the basics and want to review your state of knowledge and get a bit deeper in to the subject. Otherwise (particularly if you are fitting this in to your other activities), regard the time estimates for the assignments as wildly optimistic: multiply by 150% and use the next highter time unit.

But don't let that put you off, GANs aren't easy whichever way you look at them (unless you invented them)

By Daniel Y

Feb 22, 2021

This is generally a good course to take. However, compare to the Deep Learning Specialization, there are few lacking points. First, the course touches only high-level concepts, which is good in some point but I expected more low-level as well. Second, Sharon speaks way too fast. Later in the course, I set the speed as 0.75x and it was better. I feel like Andrew spoke little slow in Deep Learning courses and now I feel slower is better than fast. Lastly, I hope that the course offers ppt slides available so that we can refer to it later. Moreover, some slow handwriting interaction would be good (like Andrew).

By AhmedAbdel-Aal

Oct 15, 2020

The course is a great introduction to GANs. The explanation was simple and to point and the slides are great with the key points in the first few seconds and also with the summary at the end. However, there are some points that I did not like throughout the course. 1- some concepts that need to be well disgusted are just thrown in a 2 min video, and no matter how I repeat that video, I still can't get it because it is not so intuitive, so some points need more explanation ex: Wasserstein loss. 2- The assignments were not so helpful, I guess you should let the learner to code more than that.