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.
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.
By Ibrahim G•
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•
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•
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•
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•
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•
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•
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•
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•
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•
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•
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•
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•
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•
Sharon Nailed it on the insights and the intutions behind every concept discussed and their visual and crisp clarity reasonings
A very good course to understand the basics of how GANs work, but sometimes mathematical explanations were lacking
By Arunava M•
I think the videos could have been a bit longer and more technically detailed, nonetheless an enjoyable course!
By Nicholas M C•
It would be better if the assignments provided much less of the code, so that people could struggle more.
By Suvojyoti C•
Very exciting course content! Only if could give a primer on PyTorch - that would be awesome
By Yudun W•
A very easy to understand guide for those who are interested in how GAN generally works!
By Nicola P•
Exceptional theoretical part, but mandatory assignments are way too simple
By Venu V•
More help (and annotations) on the code beyond start/end blocks would help
By Niraj S•
Loving it so far. Kudos to Eda Zhou. She is an excellent instructor.
By Oguzcan B•
It was very sufficient way to learn Basics of GANs for me.
By Karan S•
It would have been nice to have the course in tensorflow.
By Samuel h•
hope the tasks could be more challenging with more hints.