Amazing Course

Great Course!

Great course.

Great course!

Excellent!!!!

great topics

It was good!

Amazing work

Good course

Excellent!

superb...!

Excelent!

Awesome!

Very good

Loved it.

great !!

Awesome!

amazing

Thanks

Good

nice

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)

Great material so far, the lecture videos are clear and concise. And (most) of the reasoning behind the mathematics and decisions are explained, so you would ideally be able to then go and engineer your own ideas and understand why you make the decisions you do. The addition of extended learning resources was also useful (e.g. published papers) - I would love to see more of this kind of teaching, where the lessons equip you to be able to read, understand and implement material from research papers. Since deep learning is (still) an extremely active field of research, every practitioner needs to have the tools available to keep learning and understanding

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.

good intro to GANs, but some topics are really just touched upon (e.g. W-loss); I would have appreciated a longer course with more videos and more depth. The teacher is clearly very knowledgeable, but I agree with other reviewers that she's speaks too fast. Grading exercises are pretty basic, with only few lines of code to be filled in between the placeholders; the good thing is you can download the Jupyter notebook and it is a good reference for further development/study.