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Avis et commentaires pour d'étudiants pour Apply Generative Adversarial Networks (GANs) par

396 évaluations
83 avis

À propos du cours

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....

Meilleurs avis

5 déc. 2020

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 janv. 2021

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!

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51 - 75 sur 85 Avis pour Apply Generative Adversarial Networks (GANs)

par Samuel K

4 mars 2021

Awesome course! Direct application to my research!

par nghia d

21 déc. 2020

amazing course! thanks coursea, thanks Instructors

par Евгений Ц

31 janv. 2021

Easy yet fundamental enough for an eager learner.

par Shams A

23 juil. 2021

Amazing course. Thanks so much for offering it!

par Ali G

22 juil. 2021

Very informative and easy-to-understand!

par Gokulakannan S

26 déc. 2020

Nice course enjoyed it a lot. Thanks!

par James H

17 nov. 2020

Very thorough and clearly explained.

par Xiaoyu X

1 août 2021

Very good lectures and assignments!

par Jesus A

22 nov. 2020

Great applications cases of GANs

par Dela C F S

6 juin 2021

Full of amazing content! :D

par Manuel R

30 mars 2021

It was a nice experience!

par amadou d

11 mars 2021

Excellent! Thank You all!

par brightmart

11 nov. 2020


par Cường N N

8 déc. 2020

This course is very good

par 晋习

17 oct. 2021

data augment is helpful

par M. H A P

7 avr. 2021

What a great course

par Diego C N

1 nov. 2020

An amazing Course

par Tim C

8 déc. 2020

Incredible! :)

par Vishnu N S

26 juil. 2021

Great Course

par vignesh m

26 nov. 2020


par Kuro N

25 juil. 2021


par Raymond B S

14 févr. 2021

Thank you

par Steven W

26 févr. 2021

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.

par Ernest W

8 janv. 2022

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.

par Harold S

6 mars 2021

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.