Chevron Left
Apply Generative Adversarial Networks (GANs)(으)로 돌아가기

deeplearning.ai의 Apply Generative Adversarial Networks (GANs) 학습자 리뷰 및 피드백

4.8
별점
398개의 평가
84개의 리뷰

강좌 소개

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....

최상위 리뷰

UD
2020년 12월 5일

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
2021년 1월 23일

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!

필터링 기준:

Apply Generative Adversarial Networks (GANs)의 86개 리뷰 중 51~75

교육 기관: Shivender K

2021년 1월 24일

Very complex specialization but significantly helpful

교육 기관: Samuel K

2021년 3월 4일

Awesome course! Direct application to my research!

교육 기관: nghia d

2020년 12월 21일

amazing course! thanks coursea, thanks Instructors

교육 기관: Евгений Ц

2021년 1월 31일

Easy yet fundamental enough for an eager learner.

교육 기관: Shams A

2021년 7월 23일

Amazing course. Thanks so much for offering it!

교육 기관: Ali G

2021년 7월 22일

Very informative and easy-to-understand!

교육 기관: Gokulakannan S

2020년 12월 26일

Nice course enjoyed it a lot. Thanks!

교육 기관: James H

2020년 11월 17일

Very thorough and clearly explained.

교육 기관: Xiaoyu X

2021년 8월 1일

Very good lectures and assignments!

교육 기관: Jesus A

2020년 11월 22일

Great applications cases of GANs

교육 기관: Dela C F S

2021년 6월 6일

Full of amazing content! :D

교육 기관: Manuel R

2021년 3월 30일

It was a nice experience!

교육 기관: amadou d

2021년 3월 11일

Excellent! Thank You all!

교육 기관: brightmart

2020년 11월 11일

GREAT COURSE AT COURSERA!

교육 기관: Cường N N

2020년 12월 8일

This course is very good

교육 기관: 晋习

2021년 10월 17일

data augment is helpful

교육 기관: M. H A P

2021년 4월 7일

What a great course

교육 기관: Diego C N

2020년 11월 1일

An amazing Course

교육 기관: Tim C

2020년 12월 8일

Incredible! :)

교육 기관: Vishnu N S

2021년 7월 26일

Great Course

교육 기관: vignesh m

2020년 11월 26일

Wonderful!

교육 기관: Kuro N

2021년 7월 25일

Amazing!!

교육 기관: Raymond B S

2021년 2월 14일

Thank you

교육 기관: Steven W

2021년 2월 26일

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.

교육 기관: Ernest W

2022년 1월 8일

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.