deeplearning.ai による Apply Generative Adversarial Networks (GANs) の受講者のレビューおよびフィードバック
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人気のレビュー
I really liked the exposure to preparing various loss functions in paired and non-paired GANs, introduction to other applications, and many great changes to improve the quality of the networks!
GANs are awesome, solving many real-world problems. Especially unsupervised things are cool. Instructors are great and to the point regarding theoretical and practical aspects. Thankyou!
Apply Generative Adversarial Networks (GANs)： 51 - 75 / 84 レビュー
by nghia d
•amazing course! thanks coursea, thanks Instructors
by Евгений Ц
•Easy yet fundamental enough for an eager learner.
by Shams A
•Amazing course. Thanks so much for offering it!
by Ali G
•Very informative and easy-to-understand!
by Gokulakannan S
•Nice course enjoyed it a lot. Thanks!
by James H
•Very thorough and clearly explained.
by Xiaoyu X
•Very good lectures and assignments!
by Jesus A
•Great applications cases of GANs
by Dela C F S
•Full of amazing content! :D
by Manuel R
•It was a nice experience!
by amadou d
•Excellent! Thank You all!
by brightmart
•GREAT COURSE AT COURSERA!
by Cường N N
•This course is very good
by 晋习
•data augment is helpful
by M. H A P
•What a great course
by Diego C N
•An amazing Course
by Tim C
•Incredible! :)
by Vishnu N S
•Great Course
by vignesh m
•Wonderful!
by Kuro N
•Amazing!!
by Raymond B S
•Thank you
by Steven W
•I would have preferred the assignments spent more time on the training loop, and talking about what's going on with the cost function.
One of the interesting things about GANs is that your cost function is different for different parts of the network. This is really really important to the workings of a GAN, but we never touched the training loop after the first assignment in course 1. I feel like we should have spent more time nailing that training loop down.
Also, I don't think any of the classes mentioned the importance of the fact that the cost function is learned, rather than explicit. That's huge! You can do that for any network, not just generative networks, and it seems applicable to all kinds of less-supervised ML. It seems a waste that they didn't draw more attention to that.
by Ernest W
•Overall it was good but the final assignments were very confusing in my opinion because there are so many things going on there I still don't understand. I still think there is a lot to supplement, hours of exploration and reading many research papers to meet my expectations so I can create own generative art. Maybe more similar assignments with more detailed explanations (and more tasks) would make me understand more even at the cost of the specialization duration.
by Harold S
•It was good, I think it covered a lot of material and get you fast to a point where you can start attacking some real problems with this technology, however I do not fully like some of the exercises that get you stuck with some silly things.
by Stanislav K
•The course material is of very good quality. On the other hand, most of the coding exercises are limited to implementation of the loss functions. They are not teaching the students how to design the GAN architectures yourself.
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