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

4.7
звезд
Оценки: 539
Рецензии: 83

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

GJ

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!

AB

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.

Фильтр по:

51–75 из 81 отзывов о курсе Build Better Generative Adversarial Networks (GANs)

автор: amadou d

9 мар. 2021 г.

Excellent and Fantastik. Thank You!

автор: Kenneth N

27 июня 2022 г.

exceptional and clear instructions

автор: Gabriel O

25 нояб. 2021 г.

V​ery nice course!

автор: DO D T

25 янв. 2021 г.

MANY THANKS TO YOU

автор: Ms. N A A

14 дек. 2020 г.

Great clear course

автор: Vishnu N S

9 июля 2021 г.

Great Course !!!

автор: Tim C

8 дек. 2020 г.

Great stuff ! :)

автор: Stefan O B

27 янв. 2021 г.

great course!

автор: Jason C

1 янв. 2021 г.

Great stuff!

автор: Vignesh M

25 нояб. 2020 г.

Wonderful!

автор: SUMIT Y

24 нояб. 2020 г.

SUPER!!

автор: Toni P

28 мар. 2021 г.

Great

автор: Mark L

25 нояб. 2020 г.

I enjoyed the course and believe I learned a *little* of the material presented. One thing that I'd find helpful in the programming notebooks for the exercises is to add a little more descriptive material, either in text or code comments. I was lucky that I was able to complete the exercises, but often they required adding "print" statements to understand what was going on. I generally found the optional labs to be less valuable since they either couldn't be meaningfully executed, or presented contrived random results that were not very meaningful (see comments in https://deeplearningaigans.slack.com/team/U01BR86L13M for example).

автор: ARTEM B

6 мар. 2021 г.

In my opinion, all those `optional` papers just add unnecessary buzz to the studying process. If you think some particular paper is something really important, then better to do a video about this with explanation. Information should be presented in a structured way for better contribution to students intuition about the matter. Honestly, after the second course I feel a bit dizzy.

автор: Stijn M

14 янв. 2021 г.

Again I love the content, the information and everything in it. I dislike the "difficulty" of the exercises. Yes, the content in it is great but passing them does not necessarily mean you understood what you're doing.

автор: Ulugbek D

24 нояб. 2020 г.

I think this course has more advanced "tricks" and models that are supported with fewer assignments, which could be one shortcoming of the course.

автор: Rishab K

2 июня 2021 г.

StyleGAN part is awesome although fairness in AI also took a lot of time which i didn;'t expected

автор: Bharath P

20 окт. 2020 г.

Excellent course. Week 2 could have been better by talking more about Machine learning bias

автор: Ben K

5 авг. 2021 г.

Interesting subject, nice presentation, assignments are not intuitive

автор: Ernest W

7 янв. 2022 г.

Valuable but also far from perfect. In week 3 focused on StyleGAN, programming assignments show its structure but nothing further. I feel disappointed a bit as we didn't use StyleGAN to generate anything. I hope next course in specialization will further explore image creation and meet my expectations or it may be too difficult to code on my own and read all the included papers (as homework).

автор: jayce_hu

31 мар. 2021 г.

IN week three, most of the component of stylegan have a clear explanation, that is good. But it lacks the overall code architecture, how to link the generator with the discriminator in trainning process? how to stable the progress trainning in styleganit's important to get intuition about how stylegan work.

автор: Iván G

6 нояб. 2020 г.

There are concepts which should be explained with more details, such as the content of StyleGAN (Week 3). The instructions of the 2nd week - notebook are not clear. Nevertheless, the course provides a good first approach to the state of the art of GANs.

автор: Moustafa S

15 окт. 2020 г.

the assignments where not that helpful, even tho the comments where a course on it's own, but when solving the assignment it may take you 4 hours just to learn the way the function works, which is the biggest issue in pytorch and scipy

автор: Kyle S

14 нояб. 2021 г.

You have to really love GANs, or have a real immediate need for them, to enjoy this course. All the earlier DEEPLEARNING.AI courses were pure joy, and not as much of a grind.

автор: Алексей А

25 янв. 2021 г.

Week 2 is pretty raw - much reading and few explanation within lectures. After that programming tasks look like game "guess what to do to pass".

Lecturer speaks too fast.