Learner Reviews & Feedback for Build Basic Generative Adversarial Networks (GANs) by DeepLearning.AI

The course is pretty awesome for a beginner who is trying to understand the world of GANs. It provides a good deal of theory lectures and inspires the need for GANs by showing the areas in which they're used with examples.

The exercises, although good aren't sufficient; in the sense we're only required to tweak a very small amount of code and the boilerplate for most code is given. But the exercises as a whole are really cool!

Great lectures and exercises in "digestible portions". The course explained the GAN basics first and then built upon that base knowledge in a gentle and well though way. You always think that by just reading papers and reviewing reference implementations you can master some new ML-area, but this kind of course is so much more fun with materials, community and support.

This course is great view into GANs. The lectures often briefly review the basics of topics like neural nets or convolutions, yet still offer advanced (optional) lessons and journal articles to read.

I rated 4 stars instead of 5 because I could not find printable/PDF notes for the course, unlike some other courses.

Sharon does a great job of teaching concepts, and the course follows well from the Deep Learning Specialization. You will find that while the code exercises start out facile, you will require some help on the Slack channel, almost no code support is given in course (in contrast to Nanodegree programs).

I think this is a bit too basic, there are some areas where i believe some more maths and theory might be appropriate. IE specifically the video section prior to W4B programming exercise with the latent z space manipulation to target disentanglement of features.

Good for basic GAN knowledge. Good for Pytorch knowhow, if you are new to it. Concepts are explained in easy to understand way.

More mathematical explanations on probability distributions of real and fake images, Their distances would have been better

FYI to course creators...

Almost without exception, the correct answer to the quiz questions was the longest answer. I know the quizzes are not graded, nevertheless the consistency of this pattern reduces the value of the quizzes as a learning tool.

The course was very good, only complaint is that assignment w4b was a little vague, in terms of comments on the code and even the fact that no paper or explanation was offered in the course for in depth implementation of the algorithm.

Great material and instructors. Enjoyed watching videos and taking assignments.

Assignments could have been more difficult if we assume most people have taken the deep learning specialization or are familiar with deep learning.

Thanks

I love the explanation and what you actually do in this course. However, if I were to use this to evaluate whether a candidate for a job can work with GANs in practice, I think the complexity for passing the exercises is too low.

Nice material, but the assignments are extremely rudimentary (paint by numbers/fill in the blanks). Perhaps you could provide more advanced (even ungraded, if that's the challenge) assignments for folks that want them?

Thanks

The voice over was pretty fast and hard to understand, so had to do lots of sliding window in video to understand the topics. Otherwise this course is fantastic gateway to understand GAN and it's applicability.

Great intro course, the programming assignments were pretty weak in difficulty level, could have had less hand holding there. Excited to get into more high resolution GANs soon!

A little lacking in technical knowledge. You just get to build a GAN and understand bits and pieces about why it works in very simple terms, little mathematics involved.

Great course to start building GANs.

I wish more math was included. I realize the math behind this is very complex, and not everyone wants to know about that.

Great: a motivating teacher and well-structured learning material. It would be cool to provide the slide sets and to eliminate the need to use Slack.

Excellent example code and assignments. Overall great course, only suggestion but would be adding a little more depth in the lecture topics.

Good and quite quick course. Assignments very focused on the innovation of the week, which makes them very short and not very demanding.

The course was too good and knowledgable. But I felt the loss calculation of the disentanglement should have been explained in detail.

There should be some explanation of the assignment's code. The lectures were precise and intresting. I like it. It was informative.

Sharon Nailed it on the insights and the intutions behind every concept discussed and their visual and crisp clarity reasonings

A very good course to understand the basics of how GANs work, but sometimes mathematical explanations were lacking

I think the videos could have been a bit longer and more technically detailed, nonetheless an enjoyable course!

It would be better if the assignments provided much less of the code, so that people could struggle more.

Very exciting course content! Only if could give a primer on PyTorch - that would be awesome