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Отзывы учащихся о курсе Build Basic Generative Adversarial Networks (GANs) от партнера deeplearning.ai

4.7
звезд
Оценки: 1,513

О курсе

In this course, you will: - Learn about GANs and their applications - Understand the intuition behind the fundamental components of GANs - Explore and implement multiple GAN architectures - Build conditional GANs capable of generating examples from determined categories 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

HL

10 мар. 2022 г.

Great introductory to GANs, focused on the building blocks to neural net/ GANs, and a bit of frequently used models. Might need a small update on what's considered "state-of-the-art" in the course.

WM

1 окт. 2020 г.

The course provides good insight into the world of GANs. I really enjoyed Sharon's explanations which were deep and easy to understand. I really recommend this course to anyone interested in AI.

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351–372 из 372 отзывов о курсе Build Basic Generative Adversarial Networks (GANs)

автор: Gustavo J M

24 дек. 2020 г.

No se condice la pretendida profundidad de las explicaciones con las prácticas en código. Preferiría ir de a poco y más lentamente y dejar más claros los conceptos clave. La instructora es muy amable pero la velocidad del inglés es imposible de seguir para quienes no somos nativos.

автор: Henrik S

17 апр. 2021 г.

The overview of several types of GAN with their potential issues that may arise, was good.

However, I would like to see the mentors more active in the discussion groups. I still have questions, that would have been answered quite easily by the mentors. That would have been great.

автор: Adib B

8 февр. 2022 г.

Thanks Coursera and DeepLearning.AI for providing this condition for all Enthusiasts.

This course would have been much better if the teacher had spoken a little slower, the scripts helped me a lot but there were some missing words in them.

автор: Andrea B

19 дек. 2020 г.

The theoretical concepts are explained in a clear way, even if I would have liked a deeper dive into the math behind the loss functions of each model proposed, moreover the assignments were too guided imo.

Nice course overall!

автор: Quarup B

17 февр. 2021 г.

Informative, but it feels like it didn't include explanations (or at least intuitions) required to fully grasp the concepts. For example, the necessity of 1L continuity and why does the enforcement work.

автор: yuan

9 сент. 2021 г.

The videos teaches GAN, which is great, but the lab train for pytorch, which is great as well. But I wish the video and the lab works together so we can apply what we learn from the video into labs.

автор: Naveed M

1 июля 2021 г.

The programming assignments can be improved by designing it in such a way that most of the work should be done learner not by the course designer. I hope you change it in future.

автор: Aaron S

18 апр. 2021 г.

Basically good, however the programming assignments are incredibly trivial compared to other machine learning courses I've taken on Coursera.

автор: YutaoLAN

9 окт. 2020 г.

be unfamiliar with english and unlike Andrew use mathematical formula , so i learn a little hard

автор: vishal

1 авг. 2021 г.

Can be more detail. In week 3 and 4, there is not much information shared/taught.

автор: Bedrich P

21 авг. 2021 г.

I don't like the style of programming assignments, otherwise good

автор: Michael K

12 окт. 2020 г.

Great intuitive explanations but it is too easy

автор: Keebeom Y

17 авг. 2021 г.

She talks too fast! Please slow down!

автор: Ivan V S

23 авг. 2021 г.

T​he idea of the course is great, but the realization is TERRIBLE!!! :( That wierd chinese schoolgirl mumbling 'bout kitties and golden retrievers insted of giving instruction on programming is just annoising!!! The tasks are completely diverse with the lectures and very demotivating. :( I'm really disapointed after this course. :( Conclusions: try this only if You're ready to spend a lot of time googling the answers and cursing.:( WE WANT LAURENCE!!! WE WANT LAURENCE!!! WE WANT LAURENCE!!!

автор: Christoffer M

4 мар. 2021 г.

The GANs in the course are basic as advertised, but unfortunately the treatment of the theory is basic and shallow as well. The lab assignments are too simplistic to force any deeper understanding.

автор: Daniil K

28 авг. 2021 г.

T​he course if very interesting, but unfortunately after the completion you lose the access to assignments and the only way to restore it is to subscribe again.

автор: Fatemeh A

11 июня 2021 г.

It was too high level without mentioning the math behind the theories. The codes were too simple and not challenging. The instructor was speaking too fast.

автор: Yu G

17 янв. 2021 г.

Homework size are TOO large! One star given. One additional for that this course is highly challenging.

автор: Daniel J

27 февр. 2021 г.

The content is clear but lacks any real depth. Any time a more difficult topic pops up the details are completely ignored or swept under the rug without any acknowledgement. Even a comment like "this topic is beyond the scope of what we want to cover here, go to this resource to learn more..." would have been far preferable. This seems to be a recurring theme in recent specialisations by deeplearning.ai rather than the fault of this particular instructor.

автор: Ranga R S

11 февр. 2021 г.

Had to pause multiple times to listen again or read the English translation at the bottom. Slowing down the lecture along with proper pauses and meaningful visual illustrations can improve this course in a big way.

Content of this course is good, but the way it is presented leaves much to be desired

автор: Michael S

7 февр. 2021 г.

The coding exercises seem completely unguided by the course, and feel like a waste of my time.

I'm not going to pay you for the time I spend studying pytorch.org

автор: Scott A

20 июля 2021 г.

Way, way, way too light on the details