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Отзывы учащихся о курсе Apply Generative Adversarial Networks (GANs) от партнера

Оценки: 400
Рецензии: 85

О курсе

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....

Лучшие рецензии

5 дек. 2020 г.

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!

23 янв. 2021 г.

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!

Фильтр по:

51–75 из 86 отзывов о курсе Apply Generative Adversarial Networks (GANs)

автор: Shivender K

24 янв. 2021 г.

Very complex specialization but significantly helpful

автор: Samuel K

4 мар. 2021 г.

Awesome course! Direct application to my research!

автор: nghia d

21 дек. 2020 г.

amazing course! thanks coursea, thanks Instructors

автор: Евгений Ц

31 янв. 2021 г.

Easy yet fundamental enough for an eager learner.

автор: Shams A

23 июля 2021 г.

Amazing course. Thanks so much for offering it!

автор: Ali G

22 июля 2021 г.

Very informative and easy-to-understand!

автор: Gokulakannan S

26 дек. 2020 г.

Nice course enjoyed it a lot. Thanks!

автор: James H

17 нояб. 2020 г.

Very thorough and clearly explained.

автор: Xiaoyu X

1 авг. 2021 г.

Very good lectures and assignments!

автор: Jesus A

22 нояб. 2020 г.

Great applications cases of GANs

автор: Dela C F S

6 июня 2021 г.

Full of amazing content! :D

автор: Manuel R

30 мар. 2021 г.

It was a nice experience!

автор: amadou d

11 мар. 2021 г.

Excellent! Thank You all!

автор: brightmart

11 нояб. 2020 г.


автор: Cường N N

8 дек. 2020 г.

This course is very good

автор: 晋习

17 окт. 2021 г.

data augment is helpful

автор: M. H A P

7 апр. 2021 г.

What a great course

автор: Diego C N

1 нояб. 2020 г.

An amazing Course

автор: Tim C

8 дек. 2020 г.

Incredible! :)

автор: Vishnu N S

26 июля 2021 г.

Great Course

автор: vignesh m

26 нояб. 2020 г.


автор: Kuro N

25 июля 2021 г.


автор: Raymond B S

14 февр. 2021 г.

Thank you

автор: Steven W

26 февр. 2021 г.

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.

автор: Ernest W

8 янв. 2022 г.

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.