I completed all three courses for the GAN specialization. Overall, this is an excellent course. The content is high quality and compact. The course is highly recommended for professionals who have limited time to keep up with the state-of-the-art in GANs. I feel that the course has given me enough knowledge for me to find ways to apply these skills for good in the industry.

Areas for possible improvement: 1. Some of the lab exercises put focus on the wrong areas. In some cases, I feel like I was spending time on tensor manipulation instead of learning the important nuances of the algorithms. 2. I would love to see the course extended. It's relatively short and I think some of the advanced optional content could be incorporated into the standard curriculum. What I value most from this course is how it condenses and simplifies concepts. The optional content leaves the reader to self study and doesn't help with accelerating learning. Insights that help the learner understand the architecture differences, improvements as well as the pros/cons of the GANs referenced in the optional content would be valuable.

Just completed all 3 courses. Overall it's fun to learn and play with GANs. The labs are surprisingly well designed and make it easy to get started. Even with prior knowledge in this area, I still find it valuable and informative to catch up with recent research progress, many of the cited works are published within a year. Great learning experience.

It is just great hearing the subject from a PhD owner . This course is just the right length and right difficulty for anyone who really wants to broadly "understand" the already broad subject for his/her job or research goals.

Nice explanations. All you need to know about the state of the art in GANs.

Why do you need to start a course by insulting your students with some "oath"? You don't own the knowledge: there are github repositories and papers available online. All we need is a good introduction to the topic. Which you did provide, by the way, perhaps not as detailed as I wanted, but there was interesting material.

I don't understand the purpose of listing a handful of research papers and not teaching the topics

If you have better options skip this, it will save your time and money.

I've just completed the specialization and my thoughts are that everyone should take it (that are interested in GANs! I feel Sharon is a great teacher and the entire team did a really good job on putting togethor these courses. After completing it I definitely have a much better view of GANs, their architectures, successes and limitations, and have a solid background to tackle reading papers and implementing them on my own. Thank you for making this specialization!

With all the positives (which is why I rate it 5/5) there are in my opinion things that can be improved. Especially I think there is too much hand holding for the labs, out of 100 rows of codes I code maybe 2-3%. Many of these don't give much value coding but I want to feel like I did it! Unfortunately now I am left guessing if I have truly mastered the material (and I'm quite sure I haven't, so I will need to re-implement these on my own). Also since you state that calculus and linear algebra are prerequisites then stick with it! You are trying to be too inclusive and there are several part of the courses where I thought it was entirely unecessary because everyone taken Calc and Linalg already has this knowledge. I would prefer instead if you spend this time making other videos where you go in more depth, perhaps going through some of the difficult math etc. Hopefully you try to improve this for future courses done by deeplearning.ai

This was the most challenging of the series so far. It was really great at not hand-holding as much in the programming exercises you that you get a better learning experience of actually struggling through creating your loss functions and compiling your neural network. If I could add one improvement, it would be to include some sort of capstone project wherein we would be required to implement one of the GAN architectures taught (DCGAN, StyleGAN, PatchGAN, or CycleGAN) in our own dataset or perhaps a different dataset. This would be quite challenging as the code would not be provided in terms of how to compile the network and training loops needed. This may also serve as a final challenge to figure out if we have really conceptually absorbed the different architectures and their respective limitations/implementations.

A really nice course which introduces some of the most recent architectures and applications of GANs. The programming assignments are meticulously crafted to help solidify the concepts that were taught during the week. The instructor does a pretty good job at explaining different concepts in an engaging way!

Very nice course for someone who is familiar with the basics of ML and wants to study GANS. For someone who wants to start their own GAN project, code assignments are really useful, as they contain transparent and reusable pieces of code to quickly start training your own GANs. Thank you

Really interesting and informative! I'm amazed by all the cool things that GANs can do! The exercises were fun, and the help from Slack, Particularly from Paul Mielke, was very useful. I hope Coursera will offer other courses on GANs and other generative approaches.

Awesome course to learn a lot about very cool GAN applications! All the material is very well designed, and the assignments really let you get a good practical insight on the different topics covered during the lessons. Thank you so much to everyone

This Course was fun to do and was also very much helpful for my knowledge. Mainly the reading part was very good and had so much to study and gain from which I think was best and also the video lectures and Assignment notebook off course.

I really liked the exposure to preparing various loss functions in paired and non-paired GANs, introduction to other applications, and many great changes to improve the quality of the networks!

Great course and the specialization! It gives a clear explanation of quite difficult concepts, after which it becomes much easier to look for more details in original papers.

It's a great specialization and I deeply enjoyed it! I want to thank Sharon and her team of developing this material! I highly recommend it!

Great course, it provides an excellent explanation on concepts and provides useful practical exercises on main applications of GANs.

It was fun to learn, especially cycle gan part. I only hope the authors will keep creating new courses. Looking forward to them.

great course and great material really, keep the great work and hopefully seeing more of your courses again Zho <3

Very good and interesting course where you learn how state of the art GAN's is constructed.

The course did a great job of conveying complex material very succinctly and clearly.

Great course and a fantastic Specialisation! Would recommend to everyone interested!

A wonderful course, which serves as a great conclusion to the specialization.

Great content. Thanks a lot for creating this wonderful course. :)