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: 1,513

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:

126–150 из 372 отзывов о курсе Build Basic Generative Adversarial Networks (GANs)

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: G.H L

11 апр. 2021 г.

It was really helpful for building my deep generative modeling skills with details and mindful examples.

автор: Rohak S

21 февр. 2021 г.

Amazing to see how the curriculum designers have distilled cutting edge resource into an online course

автор: SUMIT Y

15 нояб. 2020 г.

NIce course for those who want to start his/her journey towards GAN, and want to learn MAth behind it.

автор: 黃東聖

3 мар. 2022 г.

A great introduction! I can easily get the general concepts about basic GANs with affordable homework

автор: Tze C L

19 мая 2021 г.

An easy and friendly introduction to building your very own Generative Adversarial Networks (GANs).

автор: Sinan D

23 дек. 2020 г.

Good course but better if you get Deep Learning and CNN courses beforehand to make this more sense.

автор: Stephen S

3 апр. 2022 г.

Great course, I enjoyed the programming assignments and the topics, look forward to the next ones.

автор: Hitesh k B

22 окт. 2021 г.

No prior background required, easy to understand notebooks, optional material for advance study :)

автор: Akhtar M

14 нояб. 2020 г.

It is awesome in many ways. The organization of this course makes you understand in a better way!

автор: Ljubiša P

27 дек. 2020 г.

Excellent. I found some of the cited papers hard to follow, but I am assuming that is expected.

автор: long s

3 нояб. 2020 г.

Very clear instructions and easy to understand metaphors (and memes!) made this course a treat!

автор: Jorge P

26 окт. 2020 г.

Muy buen curso, el contenido es de alta calidad que permite entender los conceptos en detalle.

автор: Matt S H

2 нояб. 2020 г.

Very clean explanations and programming exercises and love the knowledge checks in the videos.

автор: alon s

9 окт. 2020 г.

thank you! it was great course, learned a lot. made me realize how much potential GAN's have.

автор: Jingjian W

8 нояб. 2020 г.

didn't expect to learn wgan and how it solve the unstable problem of gan. That is impressive

автор: Jean-Marie P

25 окт. 2020 г.

Really interesting course. What was great was progressive increasing in difficult concepts.

автор: Timo J

4 февр. 2021 г.

may be irrelevant, but absolutely love Sharons manners. Very vivid and pleasant to listen.

автор: Vitalii L

20 нояб. 2020 г.

Great work! And thank you for the assistance and communication (slack).I liked the course!

автор: Dhritiman S

31 окт. 2020 г.

Good course! I enjoyed the use of PyTorch and the bottom up foundational knowledge of GANs

автор: David T

8 окт. 2020 г.

Great Introduction to the material. Assignments connected well with the lecture material.

автор: Olivier M

18 окт. 2020 г.

As usual with deeplearning.ai, amazing course. Very useful for discover the world of GAN

автор: Jing L

26 мая 2021 г.

As an introduction course for GANs it is pretty good, the assignment is a bit too easy.

автор: Xiaoyu X

22 мая 2021 г.

Great course for GANs. The assignments are really helpful. The lectures are very clear.

автор: Samrat S

7 июля 2021 г.

Very basic overview of Gan, which ignores lots of Math depth.. But good for beginners.