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

541 ratings

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


Sep 30, 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!


Mar 24, 2021

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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51 - 75 of 81 Reviews for Build Better Generative Adversarial Networks (GANs)

By amadou d

Mar 9, 2021

Excellent and Fantastik. Thank You!

By Kenneth N

Jun 27, 2022

exceptional and clear instructions

By Gabriel O

Nov 25, 2021

V​ery nice course!


Jan 25, 2021


By Ms. N A A

Dec 14, 2020

Great clear course

By Vishnu N S

Jul 9, 2021

Great Course !!!

By Tim C

Dec 8, 2020

Great stuff ! :)

By Stefan O B

Jan 27, 2021

great course!

By Jason C

Jan 1, 2021

Great stuff!

By Vignesh M

Nov 25, 2020



Nov 24, 2020


By Toni P

Mar 28, 2021


By Mark L

Nov 25, 2020

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 for example).


Mar 6, 2021

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.

By Stijn M

Jan 14, 2021

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.

By Ulugbek D

Nov 24, 2020

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

By Rishab K

Jun 2, 2021

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

By Bharath P

Oct 20, 2020

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

By Ben K

Aug 5, 2021

Interesting subject, nice presentation, assignments are not intuitive

By Ernest W

Jan 7, 2022

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).

By jayce_hu

Mar 31, 2021

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.

By Iván G

Nov 6, 2020

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.

By Moustafa 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 Kyle S

Nov 14, 2021

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.

By Алексей А

Jan 25, 2021

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.