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

4.8
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420 évaluations
87 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

UD

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!

MM

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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26 - 50 sur 88 Avis pour Apply Generative Adversarial Networks (GANs)

par Pavel K

3 févr. 2021

I really enjoyed the content of the 3rd course in this specialisation. The only wish I have for the future courses is for them to be in HD, it's 2021, come on, apply some SuperRes GANs already ;)

par Mikhail P

20 nov. 2020

Great course and the specialization! It gives a clear explanation of quite difficult concepts, after which it becomes much easier to look for more details in original papers.

par José A C C

17 janv. 2021

It is a great course that you need to take time to understand fully, particularly the optional materials and readings are super valuable to extend understanding.

par Rushirajsinh P

16 avr. 2021

Perfect course for GANs!! I've never seen such a perfect curriculum before! A blend of state-of-the-art approaches and their practical implementation!

par Lambertus d G

18 févr. 2021

Great to put the GANs to practice and see what you can achieve. This was the icing on the cake for me. Thanks Sharon for your clear explanations!

par 大内竜馬

10 mars 2021

The content is very nice. But, as a non-native English speaker, I would have been happier if you would speak more slowly, like prof. Andrew Ng.

par Yiqiao Y

5 janv. 2021

It's a great specialization and I deeply enjoyed it! I want to thank Sharon and her team of developing this material! I highly recommend it!

par Angelos K

31 oct. 2020

Great course, it provides an excellent explanation on concepts and provides useful practical exercises on main applications of GANs.

par Andrey R

7 déc. 2020

It was fun to learn, especially cycle gan part. I only hope the authors will keep creating new courses. Looking forward to them.

par Vaseekaran V

24 déc. 2021

A brilliant third course in the specialization. Really enjoyed doing this, and learned quite a lot. Thank you DeepLearning.AI

par 江昭輝

24 janv. 2022

The courses in this tutorial is awesome, very recommend for those who interested in GAN, so glad I enroll this course!!!

par Moustafa S

31 oct. 2020

great course and great material really, keep the great work and hopefully seeing more of your courses again Zho <3

par Jaekoo K

11 avr. 2021

I really enjoyed this course. It was easy to follow and clear in terms of content organizations. Thank you!

par Paul J L I

31 janv. 2021

This was a really great course, and the lectures presented really well. I learned a lot from this course.

par Akshai S

17 janv. 2021

The applications of GANs were very well illustrated in the course. I thank the coursera team for this :-)

par Stefan S

30 oct. 2020

Very good and interesting course where you learn how state of the art GAN's is constructed.

par Anri L

24 déc. 2021

S​haron Zhou, her sister and the rest of the Deeplearning.Ai team is a gift to the world!

par Arkady A

8 févr. 2021

Awesome course, with well explained material that makes state of the art new models easy!

par Dhritiman S

8 déc. 2020

The course did a great job of conveying complex material very succinctly and clearly.

par Serge T

18 nov. 2020

Great course and a fantastic Specialisation! Would recommend to everyone interested!

par Antoreep J

24 avr. 2021

Course 3 was better than Course 2. Course 2's assignments were bit confusing.

par Matthew B E R

28 nov. 2020

A wonderful course, which serves as a great conclusion to the specialization.

par Asaad M A A

13 sept. 2021

I really enjoyed taking this course. I want to thank all the instructors.

par Charlie J

26 nov. 2021

Incredible course. Thorough yet understandable for anyone interested

par Paritosh B

5 déc. 2020

Great content. Thanks a lot for creating this wonderful course. :)