I have been trying to understand and implement GANs for que a few weeks and it felt really hard but after this course made everything easy for me, deeplearning.ai has been really one of the best places to learn.

It would be helpful to include the formula of the normalization in the last parts of the assignment, as reading through the instruction is fine but having the actual regularization formula available is helpful.

T​his course is a truly amazing course. It bridges theory and practice and makes GAN easier to understand. You can also learn neat implementation of GANs that follows best software engineering practices.

Wonderful course for anyone interested in getting an introduction to GANs. All this knowledge will help me get closer to do research on state-of-the-art GAN models. Thank you for creating this material.

Excellent course, the first good thing is using PyTorch, love it , never had work with this framework and its really nice, second thing is about GANs, amazing topic I really want to learn more about it!

Great introductory to GANs, focused on the building blocks to neural net/ GANs, and a bit of frequently used models. Might need a small update on what's considered "state-of-the-art" in the course.

Great introduction of GANs. I particularly liked the programming assignments' difficulty (not too easy and not too hard). Also, the instructor is usually very clear and didactic.

Very nice course to start your acquaintance with GANs. Loved the non-obvious mention of ProteinGANs to generate protein structures.

Definetly a recommendation for the novice.

The course is great with hands-on experiments. The assignments are properly designed to let the learner focus on the most important pieces of the logic in the implementation

Good overall introduction to GANs. I really liked how well the sections on Wasserstein Loss and Conditional & Controllable GAN sections were covered in this course.

This course was awesome. Concise, simple and straightforward. The course teaches something very sophisticated but the instructor made it very easy to understand.

Awesome course. Like any other from DeepLearnin.AI, the content is given in a intuitive way, so that you can learn easily. Congratulations for the creators!

Great course! The programming assignments were a bit short and too easy. The Deep Learning Specialization assignments had the ideal difficulty and length.

I really like the way he teaches all the concept from scratch. i learn a lot

any one want to learn foundation for GAN i really recommend them this course

Sharon rocks! Very clear explanation of quite complicated material makes it relatively easy to understand GANs. Looking forward to starting course 2!

I really enjoyed taking this course on GANs. It walked me through the concepts in a reasonable speed and provided detailed explanations and insights.

It is a very nicely orgranized course that will provide a great understanding how GAN works and it's intuition with some hands on coding practices.

Excellent

Well organized, clarifies terms and concepts, high implementation

quality of assignments, impressively up-to-date on new works (Apr 2021)

This is such a great course. Explanation and guidance throughout the course was excellent. A huge thanks to our lecturer Sharon, Eric, and Eda.

This course rightly covers the introduction of both Pytorch and GANs so that the natural interest for further courses keeps increasing.

I loved it! The guided notebooks are great to make sure I am not doing any mistake and also providing unit tests in important cells.

A really good course to learn about GANs, reading the quoted research papers will help develop a better intuition and understanding.

Good job at explaining theories quickly. The assignments helped to learn pytorch and also to verify the understanding of principles.

One of the Amazing course on the Coursera Platform. Due to these courses I had choose my Final Year Project on GAN. Happy learning.

Thank you Sharon Zhou and other Instructors for this interesting course on Generative Adversarial Networks (GANs)

ThankYou Coursera.