great course, only teaching what's needed, doesn't push you a lot in the coding assignments, as much as it requires you much more work to understand the codes and the science behind it.
Excellent course. The videos were a pleasure to watch, the assignments were clear and allowed you to go as shallow or as in depth as you desired, and the mentors were very helpful.
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hard but worth it
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By S A•
It was good!
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By Martin J•
An excellent course to bring one up to speed with current developments in GANs. Quite a bit of reading around the subject, in addition to the references provided, is necessary, particularly if you are new to using pytorch or python. But the accompanying Slack support is a life line.
I think this course is even more effective if you have the basics and want to review your state of knowledge and get a bit deeper in to the subject. Otherwise (particularly if you are fitting this in to your other activities), regard the time estimates for the assignments as wildly optimistic: multiply by 150% and use the next highter time unit.
But don't let that put you off, GANs aren't easy whichever way you look at them (unless you invented them)
By Daniel Y•
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).
The course is a great introduction to GANs. The explanation was simple and to point and the slides are great with the key points in the first few seconds and also with the summary at the end. However, there are some points that I did not like throughout the course. 1- some concepts that need to be well disgusted are just thrown in a 2 min video, and no matter how I repeat that video, I still can't get it because it is not so intuitive, so some points need more explanation ex: Wasserstein loss. 2- The assignments were not so helpful, I guess you should let the learner to code more than that.
By Vinayak N•
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!
By Sami D•
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.
By Jeremy S•
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.
By Yijie X•
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).
By Sandeep W•
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.
By GAURAV A•
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