Chevron Left
Back to Build Basic Generative Adversarial Networks (GANs)

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

4.7
stars
1,867 ratings

About the Course

In this course, you will: - Learn about GANs and their applications - Understand the intuition behind the fundamental components of GANs - Explore and implement multiple GAN architectures - Build conditional GANs capable of generating examples from determined categories 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

KM

Jul 20, 2023

Helped me clarify the some of key principles and theories behind GAN and bit of history... The references/additional study materials are very useful, if you want to dig deep into. Overall very pleased

HL

Mar 10, 2022

Great introductory to GANs, focused on the building blocks to neural net/ GANs, and a bit of frequently used models. Might need a small update on what's considered "state-of-the-art" in the course.

Filter by:

351 - 375 of 437 Reviews for Build Basic Generative Adversarial Networks (GANs)

By Cameron M

•

Oct 6, 2020

Great intro course, the programming assignments were pretty weak in difficulty level, could have had less hand holding there. Excited to get into more high resolution GANs soon!

By Mahmoud T S

•

Dec 6, 2020

A little lacking in technical knowledge. You just get to build a GAN and understand bits and pieces about why it works in very simple terms, little mathematics involved.

By handy-mat

•

Feb 12, 2023

Lectures were clear, focused & informative. Assignments were well-formed with extremely helpful hints & explanations of the code to be completed. Nice work!

By Deleted A

•

Oct 16, 2020

Great course to start building GANs.

I wish more math was included. I realize the math behind this is very complex, and not everyone wants to know about that.

By Shawn

•

Nov 28, 2021

Great examples. Wish there were more reading material that bridged the gap between the papers (very detailed) and the slides (good for exposure to material)

By John F

•

May 15, 2022

An excellent course. The only area of improvement I can think of would be to get better intuition on the tensor shapes through the model building code.

By Heinz D

•

Oct 13, 2020

Great: a motivating teacher and well-structured learning material. It would be cool to provide the slide sets and to eliminate the need to use Slack.

By Rob B

•

Apr 7, 2021

Excellent example code and assignments. Overall great course, only suggestion but would be adding a little more depth in the lecture topics.

By Jonas B

•

Dec 2, 2020

Good and quite quick course. Assignments very focused on the innovation of the week, which makes them very short and not very demanding.

By Ranajit S

•

Oct 14, 2020

The course was too good and knowledgable. But I felt the loss calculation of the disentanglement should have been explained in detail.

By Laiba T

•

Jan 5, 2021

There should be some explanation of the assignment's code. The lectures were precise and intresting. I like it. It was informative.

By Priyank N

•

Oct 23, 2020

Sharon Nailed it on the insights and the intutions behind every concept discussed and their visual and crisp clarity reasonings

By Michael M

•

Oct 19, 2021

often felt I could infer what to do an assignment without understanding why I was doing it but generally great course content

By Aleksei

•

Nov 21, 2020

A very good course to understand the basics of how GANs work, but sometimes mathematical explanations were lacking

By Arunava M

•

Jan 14, 2021

I think the videos could have been a bit longer and more technically detailed, nonetheless an enjoyable course!

By Siddharta M

•

Apr 9, 2023

It's a great course. However it would have been better if there were more videos to explain the coding part.

By Nicholas M C

•

Mar 2, 2021

It would be better if the assignments provided much less of the code, so that people could struggle more.

By Mahmoud S E

•

Jun 11, 2022

Critic lessons need to be explained more in details. but overall great course with great instructor.

By ROCHETTE P

•

Mar 26, 2024

Great, missing more details on how to tune but explanations are very clear and labs are top quality

By Suvojyoti C

•

Dec 3, 2020

Very exciting course content! Only if could give a primer on PyTorch - that would be awesome

By Harry_G

•

Jan 21, 2023

The content is good for beginners who have little background, but the practice is too easy

By Yudun W

•

Dec 18, 2020

A very easy to understand guide for those who are interested in how GAN generally works!

By Aishwarya S M

•

May 31, 2023

The course was so useful. Excited to complete the next one and learn more about GANs.

By Alfredo A

•

Dec 10, 2021

Good intro to the concept felt that some of the excercises were too explicit

By Nicola P

•

Apr 4, 2021

Exceptional theoretical part, but mandatory assignments are way too simple