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.

The course is pretty awesome for a beginner who is trying to understand the world of GANs. It provides a good deal of theory lectures and inspires the need for GANs by showing the areas in which they're used with examples.

The exercises, although good aren't sufficient; in the sense we're only required to tweak a very small amount of code and the boilerplate for most code is given. But the exercises as a whole are really cool!

Great lectures and exercises in "digestible portions". The course explained the GAN basics first and then built upon that base knowledge in a gentle and well though way. You always think that by just reading papers and reviewing reference implementations you can master some new ML-area, but this kind of course is so much more fun with materials, community and support.

1. good introductory course for absolute beginners.

2. The instructor speaks very monotonically and it seems she is reading a script. Explainations could be a lot better.

3. A suggestion: Incorporate optional part in the lecture series as they contain more important and detailed mathematical explanations, which tbh i expected the instructor to cover in the course

This course is great view into GANs. The lectures often briefly review the basics of topics like neural nets or convolutions, yet still offer advanced (optional) lessons and journal articles to read.

I rated 4 stars instead of 5 because I could not find printable/PDF notes for the course, unlike some other courses.

Sharon does a great job of teaching concepts, and the course follows well from the Deep Learning Specialization. You will find that while the code exercises start out facile, you will require some help on the Slack channel, almost no code support is given in course (in contrast to Nanodegree programs).

Really excellent course I didn't have much problem following along with the lectures although some coding exercises could be made clear it was a great learning experience. There should be some more notes visualizing the generator architecture as the number of layers in the generator are not clear.

I think this is a bit too basic, there are some areas where i believe some more maths and theory might be appropriate. IE specifically the video section prior to W4B programming exercise with the latent z space manipulation to target disentanglement of features.

Very good course to understand key concepts of GANs. However, I think it would benefit from building small blocks of GANs at a time and see how we end up with a functional model, instead of giving us a notebook where we have to fill few lines of code.

Good for basic GAN knowledge. Good for Pytorch knowhow, if you are new to it. Concepts are explained in easy to understand way.

More mathematical explanations on probability distributions of real and fake images, Their distances would have been better

FYI to course creators...

Almost without exception, the correct answer to the quiz questions was the longest answer. I know the quizzes are not graded, nevertheless the consistency of this pattern reduces the value of the quizzes as a learning tool.

Perfect course. I wish we would have more detailed explanations or visualizations of the steps of GAN architectures. Also, W4 conditional GAN assignment grader could't run my code on the first even it has passed the test in the assignment.