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Вернуться к Build Better Generative Adversarial Networks (GANs)

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

Оценки: 541

О курсе

In this course, you will: - Assess the challenges of evaluating GANs and compare different generative models - Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs - Identify sources of bias and the ways to detect it in GANs - Learn and implement the techniques associated with the state-of-the-art StyleGANs 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


30 сент. 2020 г.

Very good course! Helpful to understand evaluation metrics and details of Style GAN. It was also super cool to have the bias section that is not as well known as the others. Loved it!


24 мар. 2021 г.

Great material...but the stylegan code implementation requires more video material. Instead adding one more week for ProGan part before stylegan would be helpful for the learners.

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26–50 из 81 отзывов о курсе Build Better Generative Adversarial Networks (GANs)

автор: Javier M M

22 апр. 2021 г.

Me gustaron mucho los temas en general, aunque me gustaría que en los videos hablen de las dimensiones de los tensores, a mí eso me ayudaría mucho a entender rápido

автор: Mark T

15 дек. 2021 г.

Really fun to learn. The programming assignments are good as well. They made sure I had to understand every component of different GANs. Excited for the third part


13 мая 2021 г.

The course knowledge grows like progressive grower and the knowledge I gained is making my neurons run faster thank you for such a great course

автор: Akshai S

15 янв. 2021 г.

Build state of the art models in a course is not an easy feat. Thanks to all the materials that have been provided.

автор: Akhtar M

21 дек. 2020 г.

Name explains that it is better version than previous in terms of learning and study state of the art GANs

автор: Bob K

5 мар. 2021 г.

Good course and flexible! Quick if you want that but lots of references to the papers if you want depth.

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: Rajendra A

10 авг. 2021 г.

Both course 1 and course 2 of this specialization are excellent and programming assignments as well.

автор: Amgad A

21 февр. 2021 г.

perfect instructor, perfect material, perfect sequencing for topics .. highly recommend.

автор: Csanád E

2 февр. 2021 г.

Excellent course! Great videos, somewhat challenging assignments, fantastic community.

автор: maulik p

17 янв. 2021 г.

Nice explanation of state of the art StyleGAN architecture and advanced techniques

автор: Antoreep J

18 апр. 2021 г.

I think week 2 assignment needs some better hints. Else great as always. Thanks

автор: Khushpreet S

12 дек. 2020 г.

It was much needed, thank you for bringing GAN speacialization

автор: Elemento

10 янв. 2022 г.

T​he course is really nice and gives a comprehensive understanding of GANs

автор: Chansa K

22 мар. 2022 г.

T​he presentation of the material was very exciting and easy to follow.

автор: Neeraj P

28 дек. 2020 г.

A very informative knowledge boosting course on how far GANs have come.

автор: Md. A A M

12 мар. 2021 г.

Wonderful. Really Enjoying the whole time when I start to learn GANs.

автор: Olivier M

23 окт. 2020 г.

Amazing lectures on very complex topics. Thanks a lot

автор: Deleted A

28 янв. 2021 г.

Very clear and complete overview of recent advancements in GANs.

автор: Shivender K

31 дек. 2020 г.

Highly complex and interesting course to build GAN knowledge.

автор: matan

9 дек. 2021 г.

Great course, Would be great if it would be more formal

автор: Евгений Ц

31 янв. 2021 г.

Easy yet fundamental enough for an eager learner.

автор: Jorge P

22 нояб. 2020 г.

Excelente contenido, me encantan las actividades.

автор: M. H A P

6 апр. 2021 г.

This course so helpful for my research

автор: Gokulakannan S

24 дек. 2020 г.

Nice course. Enjoyed every bit of it@