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

1,685 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


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.


Oct 1, 2020

The course provides good insight into the world of GANs. I really enjoyed Sharon's explanations which were deep and easy to understand. I really recommend this course to anyone interested in AI.

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76 - 100 of 398 Reviews for Build Basic Generative Adversarial Networks (GANs)

By Usama I B

Aug 27, 2022

This course helped me understand the foundations of GANs. It's full of coding assignments & references to original papers introducing different architectures. It is a best choice if you're absolution beginner to GANs.

By Juan P J A

Oct 25, 2020

This course introduces concepts in a clear and simplified fashion and allows to have a hands on with basic GANs models. Further insight can be obtained through the recommended papers. I look forward to the next course

By lonnie

Apr 25, 2021

This is one of the most amazing and practical Deep Learning courses I have ever taken. This course dive deep into GAN and provide many notebooks and research papers for us to practice and explore. Thank you, Sharon.

By Devavrat S B

Nov 1, 2020

I have been trying to understand and implement GANs for que a few weeks and it felt really hard but after this course made everything easy for me, has been really one of the best places to learn.

By James C N

Nov 24, 2020

It would be helpful to include the formula of the normalization in the last parts of the assignment, as reading through the instruction is fine but having the actual regularization formula available is helpful.

By Muhammad Z R

Feb 23, 2023

Nicely explains intuition behind things to implement. Walks through from most basic GAN to better versions and what improvements needed. Notebooks are good, even beginners in PyTorch can learn. Recommended.

By April P M M

Jun 21, 2021

This course is a truly amazing course. It bridges theory and practice and makes GAN easier to understand. You can also learn neat implementation of GANs that follows best software engineering practices.

By César S

Jan 18, 2021

Wonderful course for anyone interested in getting an introduction to GANs. All this knowledge will help me get closer to do research on state-of-the-art GAN models. Thank you for creating this material.

By Sebastian P

Oct 22, 2020

Excellent course, the first good thing is using PyTorch, love it , never had work with this framework and its really nice, second thing is about GANs, amazing topic I really want to learn more about it!

By Hoan L

Mar 11, 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.

By André L B V

Mar 4, 2021

Great introduction of GANs. I particularly liked the programming assignments' difficulty (not too easy and not too hard). Also, the instructor is usually very clear and didactic.

By Даниил Д Л

Mar 28, 2021

Very nice course to start your acquaintance with GANs. Loved the non-obvious mention of ProteinGANs to generate protein structures.

Definetly a recommendation for the novice.

By Wenhui L

Oct 14, 2020

The course is great with hands-on experiments. The assignments are properly designed to let the learner focus on the most important pieces of the logic in the implementation

By Ashish

Nov 1, 2020

Good overall introduction to GANs. I really liked how well the sections on Wasserstein Loss and Conditional & Controllable GAN sections were covered in this course.

By Hernandez M K J

Dec 10, 2020

This course was awesome. Concise, simple and straightforward. The course teaches something very sophisticated but the instructor made it very easy to understand.

By Rafael M

Jul 27, 2021

Awesome course. Like any other from DeepLearnin.AI, the content is given in a intuitive way, so that you can learn easily. Congratulations for the creators!

By Sebastian K

Nov 17, 2020

Great course! The programming assignments were a bit short and too easy. The Deep Learning Specialization assignments had the ideal difficulty and length.

By Arvind K V

Oct 16, 2020

I really like the way he teaches all the concept from scratch. i learn a lot

any one want to learn foundation for GAN i really recommend them this course

By Lambertus d G

Jan 9, 2021

Sharon rocks! Very clear explanation of quite complicated material makes it relatively easy to understand GANs. Looking forward to starting course 2!

By Nastaran E

Nov 10, 2020

I really enjoyed taking this course on GANs. It walked me through the concepts in a reasonable speed and provided detailed explanations and insights.

By Rajib K C

May 13, 2022

It is a very nicely orgranized course that will provide a great understanding how GAN works and it's intuition with some hands on coding practices.

By Yoel S

Apr 10, 2021


Well organized, clarifies terms and concepts, high implementation

quality of assignments, impressively up-to-date on new works (Apr 2021)

By R C

Dec 13, 2021

This is such a great course. Explanation and guidance throughout the course was excellent. A huge thanks to our lecturer Sharon, Eric, and Eda.

By Aditya A K

Dec 31, 2020

This course rightly covers the introduction of both Pytorch and GANs so that the natural interest for further courses keeps increasing.

By Rafael P

Nov 14, 2020

I loved it! The guided notebooks are great to make sure I am not doing any mistake and also providing unit tests in important cells.