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.

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

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

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

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

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

often felt I could infer what to do an assignment without understanding why I was doing it but generally great course content

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

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

It's a great course. However it would have been better if there were more videos to explain the coding part.

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

Critic lessons need to be explained more in details. but overall great course with great instructor.

Great, missing more details on how to tune but explanations are very clear and labs are top quality

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

The content is good for beginners who have little background, but the practice is too easy

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

The course was so useful. Excited to complete the next one and learn more about GANs.

Good intro to the concept felt that some of the excercises were too explicit

Exceptional theoretical part, but mandatory assignments are way too simple

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

Nice course, however with a clear focus on computer vision applications.

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

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

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

hope the tasks could be more challenging with more hints.