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Apply Generative Adversarial Networks (GANs) に戻る

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

4.8
395件の評価
83件のレビュー

コースについて

In this course, you will: - Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity - Leverage the image-to-image translation framework and identify applications to modalities beyond images - Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa) - Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures - Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research....

人気のレビュー

UD
2020年12月5日

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!

MM
2021年1月23日

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!

フィルター:

Apply Generative Adversarial Networks (GANs): 51 - 75 / 84 レビュー

by nghia d

2020年12月21日

amazing course! thanks coursea, thanks Instructors

by Евгений Ц

2021年1月31日

Easy yet fundamental enough for an eager learner.

by Shams A

2021年7月23日

Amazing course. Thanks so much for offering it!

by Ali G

2021年7月22日

Very informative and easy-to-understand!

by Gokulakannan S

2020年12月26日

Nice course enjoyed it a lot. Thanks!

by James H

2020年11月17日

Very thorough and clearly explained.

by Xiaoyu X

2021年8月1日

Very good lectures and assignments!

by Jesus A

2020年11月22日

Great applications cases of GANs

by Dela C F S

2021年6月6日

Full of amazing content! :D

by Manuel R

2021年3月30日

It was a nice experience!

by amadou d

2021年3月11日

Excellent! Thank You all!

by brightmart

2020年11月11日

GREAT COURSE AT COURSERA!

by Cường N N

2020年12月8日

This course is very good

by 晋习

2021年10月17日

data augment is helpful

by M. H A P

2021年4月7日

What a great course

by Diego C N

2020年11月1日

An amazing Course

by Tim C

2020年12月8日

Incredible! :)

by Vishnu N S

2021年7月26日

Great Course

by vignesh m

2020年11月26日

Wonderful!

by Kuro N

2021年7月25日

Amazing!!

by Raymond B S

2021年2月14日

Thank you

by Steven W

2021年2月26日

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

2022年1月8日

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

2021年3月6日

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

2021年1月31日

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.