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Learner Reviews & Feedback for Build Better Generative Adversarial Networks (GANs) by DeepLearning.AI

4.6
stars
73 ratings
10 reviews

About the Course

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....

Top reviews

MM

Oct 17, 2020

Week2 is little diverged, but concise detailed understanding explanation of style GAN is excellent. It is really worth.

VB

Oct 09, 2020

Too much attention to bias and fairness, but 3rd week is super cool!

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1 - 10 of 10 Reviews for Build Better Generative Adversarial Networks (GANs)

By Dan I

Oct 06, 2020

Worse than the first course - 3 weeks of short and high level content, gains not applicable to the outside of the course. Covering StyleGAN as an advanced architecture does not lift up the shallowness of the course in my opinion. If you are considering to enroll, I recommend waiting until the last course is released because if the first two is an indication, you can easily finish all 3 courses within the 1 week trial period and get a free specialization certification.

By Vitaly B

Oct 09, 2020

Too much attention to bias and fairness, but 3rd week is super cool!

By Matthew B E R

Oct 05, 2020

Another beautifully clear course. In particular I enjoyed FID and found A Survey on Bias and Fairness in Machine Learning an interesting read. The assignments, as always, are enjoyable, and through the unit testing give a more practical understanding of what is going on.

By German E G J

Oct 01, 2020

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!

By manohar2000

Oct 17, 2020

Week2 is little diverged, but concise detailed understanding explanation of style GAN is excellent. It is really worth.

By Olivier M

Oct 23, 2020

Amazing lectures on very complex topics. Thanks a lot deeplearning.ai

By Aniket M

Oct 21, 2020

Greate course content and assignments but I want to give one feedback to the instructor. Please keep some pause while speaking. Speak way too fast. I miss Andrew's soothing voice. Lol

By Bharath P

Oct 20, 2020

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

By Moustafa A S

Oct 15, 2020

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

By K W

Oct 21, 2020

There are too many important building blocks that aren't really being fully explained. The programming assignments should have more, small code chunks for you to complete. I don't want to just fill in the last line of code after the first 200 lines have been written for you. I would suggest following Andrew's MOOC lecture format, with many short lectures, rather than a few long lectures. I would to understand each little topic really well, not get hit with everything at once. I would prefer to have the tests at the end of each topic like Andrew's Deep Learning specialization. DeepGAN wasn't really covered at all. I was a little disappointed with the final lab, after seeing what Style-GAN can do the entire course.