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

1,246 ratings
304 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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251 - 275 of 314 Reviews for Build Basic Generative Adversarial Networks (GANs)

By Ibrahim G

Oct 20, 2020

The course was very good, only complaint is that assignment w4b was a little vague, in terms of comments on the code and even the fact that no paper or explanation was offered in the course for in depth implementation of the algorithm.

By Rustem G

Nov 25, 2020

Great material and instructors. Enjoyed watching videos and taking assignments.

Assignments could have been more difficult if we assume most people have taken the deep learning specialization or are familiar with deep learning.


By Stijn M

Jan 13, 2021

I love the explanation and what you actually do in this course. However, if I were to use this to evaluate whether a candidate for a job can work with GANs in practice, I think the complexity for passing the exercises is too low.

By Paul M

Oct 15, 2020

Nice material, but the assignments are extremely rudimentary (paint by numbers/fill in the blanks). Perhaps you could provide more advanced (even ungraded, if that's the challenge) assignments for folks that want them?


By Debdulal D

Dec 31, 2020

The voice over was pretty fast and hard to understand, so had to do lots of sliding window in video to understand the topics. Otherwise this course is fantastic gateway to understand GAN and it's applicability.

By Cameron M

Oct 6, 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!

By Mahmoud T S

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.

By Deleted A

Oct 16, 2020

Great course to start building GANs.

I wish more math was included. I realize the math behind this is very complex, and not everyone wants to know about that.

By Heinz D

Oct 13, 2020

Great: a motivating teacher and well-structured learning material. It would be cool to provide the slide sets and to eliminate the need to use Slack.

By Rob B

Apr 7, 2021

Excellent example code and assignments. Overall great course, only suggestion but would be adding a little more depth in the lecture topics.

By Jonas B

Dec 2, 2020

Good and quite quick course. Assignments very focused on the innovation of the week, which makes them very short and not very demanding.

By Ranajit S

Oct 14, 2020

The course was too good and knowledgable. But I felt the loss calculation of the disentanglement should have been explained in detail.

By Laiba T

Jan 5, 2021

There should be some explanation of the assignment's code. The lectures were precise and intresting. I like it. It was informative.

By Priyank N

Oct 23, 2020

Sharon Nailed it on the insights and the intutions behind every concept discussed and their visual and crisp clarity reasonings

By Aleksei

Nov 21, 2020

A very good course to understand the basics of how GANs work, but sometimes mathematical explanations were lacking

By Arunava M

Jan 14, 2021

I think the videos could have been a bit longer and more technically detailed, nonetheless an enjoyable course!

By Nicholas M C

Mar 2, 2021

It would be better if the assignments provided much less of the code, so that people could struggle more.

By Suvojyoti C

Dec 3, 2020

Very exciting course content! Only if could give a primer on PyTorch - that would be awesome

By Yudun W

Dec 18, 2020

A very easy to understand guide for those who are interested in how GAN generally works!

By Nicola P

Apr 4, 2021

Exceptional theoretical part, but mandatory assignments are way too simple

By Venu V

Dec 18, 2020

More help (and annotations) on the code beyond start/end blocks would help

By Niraj S

Nov 17, 2020

Loving it so far. Kudos to Eda Zhou. She is an excellent instructor.

By Oguzcan B

Mar 29, 2021

It was very sufficient way to learn Basics of GANs for me.

By Karan S

Oct 22, 2020

It would have been nice to have the course in tensorflow.

By Samuel h

Oct 9, 2020

hope the tasks could be more challenging with more hints.