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

Оценки: 397
Рецензии: 84

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!

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26–50 из 85 отзывов о курсе Apply Generative Adversarial Networks (GANs)

автор: Mikhail P

20 нояб. 2020 г.

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.

автор: José A C C

17 янв. 2021 г.

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.

автор: Rushirajsinh P

16 апр. 2021 г.

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!

автор: Lambertus d G

18 февр. 2021 г.

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!

автор: 大内竜馬

10 мар. 2021 г.

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.

автор: Yiqiao Y

5 янв. 2021 г.

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!

автор: Angelos K

31 окт. 2020 г.

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

автор: Andrey R

7 дек. 2020 г.

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

автор: Vaseekaran V

24 дек. 2021 г.

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

автор: Moustafa S

31 окт. 2020 г.

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

автор: Jaekoo K

11 апр. 2021 г.

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

автор: Paul J L I

31 янв. 2021 г.

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

автор: Akshai S

17 янв. 2021 г.

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

автор: Stefan S

30 окт. 2020 г.

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

автор: Anri L

24 дек. 2021 г.

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

автор: Arkady A

8 февр. 2021 г.

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

автор: Dhritiman S

8 дек. 2020 г.

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

автор: Serge T

18 нояб. 2020 г.

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

автор: Antoreep J

24 апр. 2021 г.

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

автор: Matthew B E R

28 нояб. 2020 г.

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

автор: Asaad M A A

13 сент. 2021 г.

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

автор: Charlie J

26 нояб. 2021 г.

Incredible course. Thorough yet understandable for anyone interested

автор: Paritosh B

5 дек. 2020 г.

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

автор: Rohan r

3 авг. 2021 г.

Very detailed study. A must learn for people working with GANs

автор: Shivender K

24 янв. 2021 г.

Very complex specialization but significantly helpful