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Back to Build Better Generative Adversarial Networks (GANs)

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

4.7
stars
632 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

MB

Aug 25, 2023

This course has helped me to dive deeper into the world of Generative AI through GANs and know what they can do and what are the advantages, benefits and disadvantages at the same time.

GJ

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!

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51 - 75 of 93 Reviews for Build Better Generative Adversarial Networks (GANs)

By Shivender K

Dec 31, 2020

Highly complex and interesting course to build GAN knowledge.

By matan

Dec 9, 2021

Great course, Would be great if it would be more formal

By Evgenii T

Jan 31, 2021

Easy yet fundamental enough for an eager learner.

By Jorge P

Nov 22, 2020

Excelente contenido, me encantan las actividades.

By M. H A P

Apr 6, 2021

This course so helpful for my research

By Gokulakannan S

Dec 24, 2020

Nice course. Enjoyed every bit of it@

By Ajeesh A

Dec 24, 2023

So detailed yet so simply explained!

By Li Y

May 28, 2023

It is very interesting and amazing!

By amadou d

Mar 9, 2021

Excellent and Fantastik. Thank You!

By Kenneth N

Jun 27, 2022

exceptional and clear instructions

By Edgar A

Jul 6, 2023

A really complete course

By Emmanuelle S

Jun 29, 2023

Excellent 2nd course

By Gabriel O

Nov 25, 2021

Very nice course!

By DO D T

Jan 25, 2021

MANY THANKS TO YOU

By Ms. N A A

Dec 14, 2020

Great clear course

By Parma R R

Mar 27, 2023

Very good 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

Wonderful!

By SUMIT Y

Nov 24, 2020

SUPER!!

By Toni P

Mar 28, 2021

Great

By ravi k

Dec 27, 2023

Good

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 https://deeplearningaigans.slack.com/team/U01BR86L13M for example).