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Вернуться к Build Better Generative Adversarial Networks (GANs)

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

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


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!


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.

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76–81 из 81 отзывов о курсе Build Better Generative Adversarial Networks (GANs)

автор: Karan S

16 мая 2022 г.

Too fast speaking, some mathematical concepts difficult to clear

автор: Michael K

6 нояб. 2020 г.

too easy

автор: Shounak D

31 июля 2022 г.

The second course was easily the low point of the curriculum. At the end, I wasnt fully able to implement Style GANs, the thing which this course sets off to do. This led to frustration and didnt feel like anything was accomplished compared to Course 1 and Course 3 where we were able to implement the whole GAN architecture. The argument: 'We have left that as assignment for students' OR 'based on the information, the students can implement the GAN' is flawed because If we students were that smart and good at it, we would have just read the papers, or watched YouTube video and implemented the paper ourselves. We wouldnt have bought a course then. But apart from that, Course 1 and Course 3 were pretty decent. We felt like we accomplished something and learnt new.

автор: Daniil K

28 авг. 2021 г.

T​he material is great; however, after the completion you lose the access to assignments and the only way to restore it is to subscribe again.

автор: Злобин Я Н

8 авг. 2021 г.

This course will have a minimum of mathematics explaining the work of GAN

автор: Bedrich P

21 авг. 2021 г.

I want to learn GANs not "fairness in ML"