Course is pretty comprehensive considering that it is the first of a trilogy covering GANs. However, I would have liked to see more math. Reading the papers did offer the depth I wanted, but I would have liked to see the lectures break down the math in a more technical way rather than stop at shallow "intuitive" explanations and elementary school analogies

Under resourced course. Instructors do not reply to questions. If you have problems with the application, you are alone with very poor support and not clear reponsibilities on who can help you. Very disappointing and frustrating. Not practical information on how to deal with custom datasets provided. It is just a tutorial with an "easy" example.

The course was a good high-level introduction to GANs. The lectures were clear and very well done, however the course lacked mathematical rigor. The in-lecture quizzes were trivial, and so were the programming assignments. This course in isolation is not sufficient to get you started with GANs in the real world.

Lectures are great, and while the assignments are generally good, the grader uses a different version than the provided notebooks. This is extremely annoying and makes the course seem unprofessionally put together, as the forums show that this problem has been known about for over a year and is still not fixed.

No se condice la pretendida profundidad de las explicaciones con las prácticas en código. Preferiría ir de a poco y más lentamente y dejar más claros los conceptos clave. La instructora es muy amable pero la velocidad del inglés es imposible de seguir para quienes no somos nativos.

The overview of several types of GAN with their potential issues that may arise, was good.

However, I would like to see the mentors more active in the discussion groups. I still have questions, that would have been answered quite easily by the mentors. That would have been great.

if you are a practiced(!) pytorch user it gives you a basic idea. if not, ... well, you'll have questions and wil spend time to understand the lines. Once I had completed, reviewed again. The code is not really well structured in week 4.

The theoretical concepts are explained in a clear way, even if I would have liked a deeper dive into the math behind the loss functions of each model proposed, moreover the assignments were too guided imo.

Nice course overall!

The videos do not allow you to understand what is really happening at the code level, they explain the general theory, but it is not something that really allows you to create a project on your own from scratch.

Informative, but it feels like it didn't include explanations (or at least intuitions) required to fully grasp the concepts. For example, the necessity of 1L continuity and why does the enforcement work.

The videos teaches GAN, which is great, but the lab train for pytorch, which is great as well. But I wish the video and the lab works together so we can apply what we learn from the video into labs.

Very interesting and comprehensive courses, but more complex skill are in optional modules, perhaps these very interesting and intricate skills are for nex modules? Good course!

The programming assignments can be improved by designing it in such a way that most of the work should be done learner not by the course designer. I hope you change it in future.

Basically good, however the programming assignments are incredibly trivial compared to other machine learning courses I've taken on Coursera.

I would have liked to have more theoretical details on the mathematical point of view of the topics covered by this course

be unfamiliar with english and unlike Andrew use mathematical formula ， so i learn a little hard

Can be more detail. In week 3 and 4, there is not much information shared/taught.

I don't like the style of programming assignments, otherwise good

Great intuitive explanations but it is too easy

She talks too fast! Please slow down!

The GANs in the course are basic as advertised, but unfortunately the treatment of the theory is basic and shallow as well. The lab assignments are too simplistic to force any deeper understanding.

The assignment on the week 3 didn't compile. Probably some version mismatch issue.

The instructor didn't go into the details of the some of the key concept, they kept it very vague

The course if very interesting, but unfortunately after the completion you lose the access to assignments and the only way to restore it is to subscribe again.

It was too high level without mentioning the math behind the theories. The codes were too simple and not challenging. The instructor was speaking too fast.

Homework size are TOO large! One star given. One additional for that this course is highly challenging.