Chevron Left
Вернуться к Build Basic Generative Adversarial Networks (GANs)

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

4.7
звезд
Оценки: 1,515

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.

Фильтр по:

101–125 из 373 отзывов о курсе Build Basic Generative Adversarial Networks (GANs)

автор: Aleks S

21 окт. 2020 г.

Good course overall. I don't feel ready to implement GANs after the assignments though because so much of the code was pre-written.

автор: George N

18 нояб. 2020 г.

So awesomely taught. Assignments were motivatingly easy and optional advanced material provided for those who want to delve deeper

автор: Bharath P

8 окт. 2020 г.

Nice Course. Lot of depth concepts were really simplified. Better to get good understanding of pytorch to follow Assignemnets well

автор: Akshai S

14 янв. 2021 г.

The course has meticulously designed for easier understanding. One has to complete the assignments to get hands on experience.

автор: Arkady A

26 дек. 2020 г.

Awesome course with clear and straightforward instruction - I felt motivated to complete this 4 week course in just two weeks.

автор: Suhaib B Y

16 мар. 2022 г.

I learned a lot from this course, just scaled down the fear which I had earlier for programming GANs with core understanding.

автор: Samuel K

5 февр. 2021 г.

Fantastic course! I have a few projects that have been in the pipeline at work using GAN's and this got me up to speed quick!

автор: Ramesh U

31 мар. 2022 г.

Outstanding course for understaing and getting practical implementation of basic GAN models, how they works and many more.

автор: Li M

4 сент. 2021 г.

like it so much, this course is challengeable and I am looking forward to diving down to the GANs model area even more.

автор: Léo Z

14 мар. 2021 г.

Exceptional course, great teaching, well organised and manageable difficulty. Graded notebooks could be more demanding.

автор: Ahmed M

10 мая 2021 г.

Great explanation and very instructive assignments. Optional challenge tasks are also available for advanced learners.

автор: Nemanja M

11 апр. 2021 г.

Really nice, I enjoyed it! You're doing an amazing job in education, please continue you beautiful work! Much love! <3

автор: Preetam P

7 февр. 2021 г.

It was so nice to join and study this course. I have get so much knowledge about GAN. It was just awesome. Thank you.

автор: Udith D B H

2 янв. 2021 г.

One of the best courses. Instructors are very helpful and the slack forum is very active. I learned a lot about GANs.

автор: Rorisang S

20 авг. 2021 г.

I​ enjoyed every lab. The simplicity with which the complex principles were expressed in code and notes is beautiful.

автор: Evgeniia C

18 окт. 2020 г.

I enjoyed this course a lot. It gave me a good understanding of gans in a quite short period of time. Very well made!

автор: Yeganeh A

6 мар. 2021 г.

Sharon is fantastic! She teaches every demanding subject in a very understandable way. I really enjoyed this course!

автор: Ivelin I

7 янв. 2021 г.

Fantastic Intro. The theoretical part is accessible and the hands on coding part Is applicable to modern app dev.

автор: Sofia T

30 нояб. 2021 г.

Really good material, explanations were clear, assignments are super cool and helpful to expand your knowledge.

автор: Shams A

16 июля 2021 г.

Amazing course! Wonderful learning experience and very helpful directions. Depth of the content was just right.

автор: Mohamed A G

24 окт. 2020 г.

Sharon did a great job explaining hard concepts. It's a great course for anyone who wants to learn about GANS!

автор: rashid a

13 авг. 2022 г.

Really good to the point explanations. Conceptual understaing and background in Neural networks is expected.

автор: Bruno A d S

16 янв. 2021 г.

I always found this subject confusing, today everything is easier and clearer. Great place to start studies.

автор: Vaseekaran V

20 июня 2021 г.

R​eally great introduction to GANs and this course serves as an amazing refresher for Deep Learning basics.

автор: Lorenzo S

22 мар. 2021 г.

Such a great course to learn Gan design basic mechanisms: a good mix of high-level concepts, math and code!