Great course with intuitive explanation of GAN architecture and components such transposed convolutional layer, leaky ReLU and etc.!

I really enjoyed this course, very compact and goes to the exact point required at this level to understand the core of GAN

Great introduction course! Really useful for beginners to get started with GANs.

Perfect GANs course.Deep explanations,useful code assignments.Thank you.

Right mix of theory, practical exercises, and most importantly fun!

Her explanation was clear but deep, I really enjoyed this course.

It will be better to provide more details for week4's content.

learned a lot about Generative Adversarial Networks

Very good introductory course

I enjoyed this course a lot

Basic introduction to GAN

This course is awesome.

really helpful

Good Introduction to GANs. Concepts are explained very well, however this course does not go into depth. But the lecturers provide you with enough references if you want to dive deeper.

The obvious philosophy of DeepLearning.ai is to make Machine Learning easy and accessible for anyone. This is an honorable goal, however it is also dangerous, because at the end of the course you might believe you have mastered GANs when in truth you did not understand much at all. For intance, in the last week I was a little tired, so instead of trying to understand each line of code, I just did the exercise, and I solved it at the first try without really understanding the code. 95% of the code is already there, you have to code less than 5% by yourself. There is not even a final exam with a longer and harder task.

The problem with these easy courses is the fact that the certificates have zero value. If it was just about the certificates, I could do the entire course in one day. No company will take coursera certificates serious because of such easy courses. At least they course creators should be more honest and declare this as a one week course.

This is generally a good course to take. However, compare to the Deep Learning Specialization, there are few lacking points. First, the course touches only high-level concepts, which is good in some point but I expected more low-level as well. Second, Sharon speaks way too fast. Later in the course, I set the speed as 0.75x and it was better. I feel like Andrew spoke little slow in Deep Learning courses and now I feel slower is better than fast. Lastly, I hope that the course offers ppt slides available so that we can refer to it later. Moreover, some slow handwriting interaction would be good (like Andrew).

有许多地方可以以补充材料的形式让学生阅读，去了解更多的理论思路或是理论的工程实现细节

The content is clear but lacks any real depth. Any time a more difficult topic pops up the details are completely ignored or swept under the rug without any acknowledgement. Even a comment like "this topic is beyond the scope of what we want to cover here, go to this resource to learn more..." would have been far preferable. This seems to be a recurring theme in recent specialisations by deeplearning.ai rather than the fault of this particular instructor.

She might be a good scientist, having excellent knowledge but not a good teacher. These slides don't even contain good stuff to be noted down. She speaks everything that should be written on the slide, whereas in the slides, we only see images and stuff. No maths behind all this is explained, it looks like a course on GANs for dummies.

Started to audit the course, but all the meaningful content is locked unless you subscribe. Pointless.

Very poor support from Mentors. They didn't answer my questions.

I hope this message finds you well. I recently completed the course on Generative Adversarial Networks (GANs) and would like to take a moment to express my appreciation and share my experience. Firstly, I would like to extend my sincerest gratitude for the opportunity to learn about GANs in such a comprehensive and engaging manner. The course content was well-structured, making complex concepts easy to understand. The hands-on approach with practical exercises and real-world examples greatly enhanced my learning experience. The knowledge and skills I gained from this course have been invaluable for my ongoing project on cancer detection using GANs. The course modules provided a deep understanding of GANs' potential in healthcare applications, and the practical assignments allowed me to apply this knowledge directly to my project. Despite facing personal challenges during the course, the engaging content and supportive community made it possible to stay motivated and continue learning. As they say, "A smooth sea never made a skilled sailor." These challenges only made the learning experience more rewarding. Now, here's a little AI-related humor to lighten the mood: Why don't machines ever laugh at jokes? Because they take things too literally! In conclusion, this course has played a pivotal role in my academic journey, and I am grateful for the knowledge and skills it has imparted. I am excited about the potential of GANs in advancing healthcare technology and look forward to applying my learnings in my future endeavors. Once again, thank you for a fantastic learning experience. Best Regards, [najmeh]

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

The course is an introduction to GANs. You won't build anything particularly powerful but it provides a springboard to the future courses in the series. This course is light on video and instruction and relies more on exercises. This is fine and possibly better since presumably you already understand neural networks well and are just looking to understand how to build GANs. If you do not have a good foundation in deep learning, you should check out Andrew Ng's courses on deep learning first.

The exercises can be easier than they should, if you will. Sometimes, the setup of the code that they give you "for free" includes critical insights. Make sure to carefully read over and understand the code outside of the few lines that you need to code for each assignment.

Also, if you are interested, I encourage you to read some of the works cited, each of which made important contributions. Focus on those that are most relevant to your work. Personally, I found "Interpreting the Latent Space of GANs for Semantic Face Editing" the most compelling.

Good class, it actually touches on mathematical aspects, and the text comprises contemporary work in the field. The programming assignments are well-designed so that, while there are usually only 10-20 lines of code to fill in (at most), one must actually think carefully about what the algorithms are doing, read the pytorch manual, and try some test scripts to make sure tensors are being handled correctly.

This is my first experience with PyTorch, and so far I like working with it better in this context than I have working with TF in other classes and books - pytorch seems to be more of a straightforward extension to the numpy / pandas / sklearn paradigm. The focus is on "what the algorithm does" rather than on "the mechanics of the framework" - although part of that may be due to instructional styles as well.

Sharon Zhou is one of the best teachers I've ever had. She (1) reaches and almost surpasses Andrew's standards, (2) has a great history of being a great learning herself (similar to Andrew) and (3) is overall infectuously enthusiastic. As an incoming softmore in University, I could say this course is likely the best resource to start learning GANS I've come accross, and I've scoured the internet.

For students that completed Andrew Ng's deep learning course or had a similar course in University, this course only builds on it and most difficult concepts are easy to grasp with the background.

I cannot speak for experts and if this course will benefit you. I'll hazard a prediction and say "yes" or "try the programming assignments and see if you could breeze through or need to learn some".