I would like to dive deeper into the GANs math more deeply, because in modern research it matters to understand ideas lying behind these methods through math. I saw some great examples of that while I was completing cs231n course from Stanford University. Would like to see more here! And also, I think that there is too little programming. I think, usually you would expect people with kind of strong (by that I mean stronger than beginner) background in DL and probably experienced in PyTorch, so in next courses I would like to see more of "hand work" with coding, because it is so important to do stuff yourself to actually learn it. Thank you guys for the great course!

This is the best course ever.

Before this course I don't know anything about GAN, but now I can understand the GAN.

In week -1 I have learned about Basics of the GAN and other Generative model and their components.

In week -2 I have learned Deep convolution GAN which is now a days used in many applications.

In week -3 I have learned about wasserstein loss and it's important to GAN.

In week -4 I have learned about Controllable generation using GAN, where we can control any of the feature of the GAN.

This course was best for me, I have learned a lot from this course,

I want to thank prof. Andrew for this Amazing Course.

While much of the basic GAN theory should be well known to people in the DL community, not many have actually had relevant hands on experience. Therefore the exercises in this course are priceless. Not only they let you avoid A LOT of boilerplate code, they also set your expectations as to what the GAN can ACTUALLY produce (often pretty bad results). Also, the course did a great job providing intuition for some of the more mathematically perplexing sections (e.g. Wasserstein GAN). Overall I would probably recommend it to a colleague.

Great course. Only thing I don't like is your try to increase level of challenging in assignments. I know that some people in reviews complain about too easy assignments with just one-line code changes. But in my opinion it's not that bad, if an assignment is well designed with emphasis on important things. In the evening after a long day at work, it can be very exhausting to spend time figuring out what params I have to pass in torch.norm to make test working. I think at least hints could be done more helpful.

En general, es un curso básico que brinda los elementos necesarios para entender el funcionamiento de las GAN. Requiere conocimientos de Python para un avance más rápido, ya que las actividades a realizar son 100% programación usando Pytorch. Al contenido le hace falta más rigor matemático, aunque se complementa con los artículos que se citan para mayor profundidad en los temas tratados. Aún así, creo que podría mejorar si se desarrollan más los puntos teóricos (en el sentido matemático).

I want to congratulate all the staff who helped to prepare the course, especially the instructor "Sharon Zhou," for her remarkable teaching expertise. When I started this course, I did not have any knowledge of generative AI, and even though I was not confident enough that I would be able to complete this course. Now, I am knowledgeable about GANs, and I have completely understood the basic concepts of GANs. It was all possible because the instructors delivered the concepts effectively.

Really amazing course (as expected from deeplearning.ai), I especially liked the detail description of almost everything in notebook assignments, Also the cool reference and advance topic. The simplified explanation of maths formula.

Also, I think infoGan paper and notebook should be moved after disentanglement video since these concept discussed in the paper are relevant to those videos.

Thank you for such an amazing course 🙂

This course is great, it presents GANs in an understandable way. The way how things are explained in each video gives a good delivery that encourages to further pursue the topic. Additional resources are included for more advanced explanations. Before choosing to start the course I've read some comments that it's too basic, maybe assignments are simple but it's not a course for someone with computer science or AI degree.

A Concise introduction to GANs! A good balance between theoretical explanations and practical implementation. Helped a lot to reach learning outcomes swiftly. Interactive jupyter notebooks are a great tool to familiarize on putting everything to work. The citations and links to respective research papers is a good approach to introduce the research practices to the pupils. Thank you for passing on the knowledge!

This course helped deepen understanding of deep neural networks. I definitely felt more command over manipulating pytorch code after this course. Course provided many helpful introduction to some widely used pytorch functions which I found really useful. And finally I actually understood basics about GAN. Thanks for this amazing course.

This is one of my favorite courses. I really like the way the instructor explained the material and thought everything was just the way I would have wanted it to be (e.g., videos, notebooks, assignments, etc...). I also really appreciate including the papers along the way so I can dig deeper as I go along. Great job on this course!

Very good introduction to generative networks. The professors explanations are really clear allow a good understanding of the theory. Notebooks provided are very simplified with lots of details for the new learner. This is not a Pytorch tutorial, but plenty of examples are provided to explore on our own.

The course was great and the slack community too. One issue was, some important topics were not introduced (vaguely introduced) in the video lectures and were asked to implement in the notebooks. Mainly, in Week 4 (for eg: regularization part). Also, the notebooks had more prewritten helping code.

This seems like the way to go. Most of the more interesting content is optional but this is for a good reason. I really enjoy that every week, each topic is complemented with papers where they originate from and more. Some of these papers are even broken down during the lectures. Quality stuff

I found this course well paced and interesting. I didn't lose any interest in the course at any point at all. Although I only knew Tensorflow and Keras when starting the course, I was able to catch up with Pytorch framework. I recommend this course to everyone interested in GANs.

I am really glad that I learned this Magical topic GANS. Thanks to all the mentors who taught this difficult topics with great ease and also to those mentors who promptly reply in the forum. Highly appreciate the Coursera community for spreading the knowledge across the world.

This is a fantastic course. It is a lot of theory to come to terms with. I appreciated the papers referenced, the clear lectures and challenging labs. It is evident that this is great jump-start, and will require substantial additional effort to ensure practical application.

I very much enjoyed this course. There are three points that I want to point out about this course:

1) The lecture is simple, but well organized.

2) The code examples/assignments are simple, but provoking more thoughts.

3) The Slack channel is really useful when you struggle.

Sharon Zhou is a great instructor and manages to keep the flow of ideas always understandable and engaging. The assignments are also perfectly crafted with helpful unit tests to make the learning experience unhindered by confusing hiccups. This is the perfect way to learn.

As a beginner to GANs, this course offers a lot of new insights that I never came across before. It helped me understand a lot of the key terms used in current state of the art research papers and helped me understand a lot of the underlying working principles of GANs.

Thank you so much for providing this wonderful course. I've learned a lot from your wonderful lectures. Specifically, I really like the way you give your lecture, very concise and interesting. Thanks again, and hopefully a lot of people can enjoy the course as well.

This course allow you to understand quite easily GANs, which I have learned are magnificent. Simply, yet with the info needed to code GANs, Sharon explained everything in a great way. I'm really happy I did this course, and can't wait to finish the specialization!

The inclusion of unit tests and hints in the programming assignments are a huge "step up" from previous Coursera programming assignments. All Coursera classes should have used this model from the very beginning. Having said that, it's better late than never.

Managed to learn the foundations of building GANs. The course is paced very well and the assignments are super interesting as well. Thank you Sharon, you are an amazing instructor! Can't wait to learn more advanced topics of GANs related to computer vision.

I loved it. It was very tough at first and I definitely need to review the code as a whole after finishing, but I learned so much in the process. Some of the courses on Coursera are so simple that they are not worth the money, but this course is worth it.