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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: 1,513

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.

Фильтр по:

151–175 из 372 отзывов о курсе Build Basic Generative Adversarial Networks (GANs)

автор: Nicolas D

13 нояб. 2020 г.

Very interesting course which helps to make a good intuition on what happens. Thanks!

автор: Sina A

30 дек. 2020 г.

Very well-organized course with easy to grasp lectures and deepening assignments.

автор: MICHAEL D S R

25 окт. 2020 г.

Excellent course management! But it is a bit too fast for non-english speakers :)

автор: John G V

6 янв. 2022 г.

G​ood Course. I enjoy how the instructors adapt theory with practical exercises.

автор: Shahir A

14 июня 2021 г.

T​hank you so much Coursera. The teacher was amazing. The problems were as well.

автор: Priscilla P L

23 янв. 2021 г.

Sharon's videos are so polished and digestible. Everything is explained so well.

автор: Luiza P

21 авг. 2021 г.

I love deeplearning.ai courses! The content and teachers are always of quality.

автор: Alessio S

1 июля 2021 г.

Useful also to understand many other aspects of Neural Networks, not only GANs.

автор: Michael C E

1 мая 2021 г.

This course was an eye opener and I have had a better understanding of GANs now

автор: FAIRUZ F S 0

26 апр. 2021 г.

Its so excited to finish this course and i also learn this for my final project

автор: Gokulakannan S

9 дек. 2020 г.

Nice Course but the interpolation technique didn't work in Week 4 assignment 1.

автор: W F

3 нояб. 2020 г.

Good content. Assignments are made to be doable in a reasonable amount of time.

автор: manohar2000

17 окт. 2020 г.

Excellent and disentangled course like the style gan. Really neat explanations.

автор: Diego M G S

7 окт. 2020 г.

una profesora increíble, muy facil de entender la teoria, no tanto las formulas

автор: Mark T

11 нояб. 2021 г.

great content. Feel like I learned a lot, and coding labs were useful as well.

автор: Marcin Z

26 нояб. 2020 г.

Great course, much better than NLP one. They use PyTorch here which is a plus.

автор: Vishal K

18 нояб. 2020 г.

Excellent course to understand the basics of GAN and also do cool assignments!

автор: Blanca H V G

18 дек. 2020 г.

Great course. Sharon is a good teacher. Thank you for all material and codes!

автор: Sangeeta O

8 нояб. 2021 г.

​Excellent course with basic of GAN ,loss functions and types of GAN covered

автор: Adam R R - A

16 нояб. 2020 г.

This was a pretty simple, understandable introduction to GANs. I enjoyed it.

автор: brightmart

7 окт. 2020 г.

Excellent! Easy to understand, and can get hand-on experience of basic GANs.

автор: pasha s

16 февр. 2022 г.

Assigngnmets make you think deeply and understand the concepts thoroughly

автор: Horváth K

9 апр. 2021 г.

I wish I could have the same quality courses at my university as well.

автор: Oleksandr M

21 дек. 2020 г.

Great course! An excellent starting point for exploring GANs. Thanks!

автор: Tim C

8 дек. 2020 г.

An absolutely fantastic course with a lot of details and applications