I like the intuitive explanation on GANS and other concepts. But it would be even nicer if the course could focus more on the theoretical side.

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

## Short version: Pretty underwhelming. ---------------------------------------------------------------------------------------------------- ## Medium version: The instructor speaks way too fast, which makes it hard to follow, especially for beginners. Most of the content stays at the conceptual level with little mathematical depth. Exercises are too simple and lack challenge. Also, there’s little explanation of PyTorch implementation details, which makes it difficult for learners transitioning from TensorFlow. ---------------------------------------------------------------------------------------------------- ## Detailed version : I normally don’t leave reviews for courses, but I just couldn't wait until I finished this one to share my thoughts—so you can probably guess how I feel about it. 1. The biggest downside: The instructor speaks incredibly fast. This becomes a real issue if you're new to GANs—you'll find yourself constantly struggling just to keep up with the pace. 2. The content: It mostly sticks to high-level ideas without diving deep into the mathematical foundations. While conceptual explanations are fine, they’re something you can easily find in blog posts online. If you want to really understand how things work under the hood, you’ll have to read the original research papers yourself—which is not always easy or accessible for everyone. Honestly, this makes me appreciate Andrew Ng even more; he has a way of breaking down complicated math from papers into digestible, beginner-friendly content—something this course lacks. 3. The assignments: They can be completed with just a few lines of code. There's not much challenge, and worse, there's very little guidance on the PyTorch code. If you’re coming from TensorFlow (like in previous DeepLearning.AI specializations), expect to face a learning curve. Some parts took me nearly 30 minutes just to fully understand.

Thanks Coursera and DeepLearning.AI for providing this condition for all Enthusiasts.

This course would have been much better if the teacher had spoken a little slower, the scripts helped me a lot but there were some missing words in them.

I don't know what the instructor is hurrying about. In terms of clarity, she is not even close to Andrew.

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.

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