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

1,016 ratings
258 reviews

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

Oct 10, 2020

great course, only teaching what's needed, doesn't push you a lot in the coding assignments, as much as it requires you much more work to understand the codes and the science behind it.

Oct 6, 2020

Excellent course. The videos were a pleasure to watch, the assignments were clear and allowed you to go as shallow or as in depth as you desired, and the mentors were very helpful.

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226 - 250 of 257 Reviews for Build Basic Generative Adversarial Networks (GANs)

By Yudun W

Dec 18, 2020

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

By Nicola P

Apr 4, 2021

Exceptional theoretical part, but mandatory assignments are way too simple

By venu v

Dec 18, 2020

More help (and annotations) on the code beyond start/end blocks would help

By Niraj S

Nov 17, 2020

Loving it so far. Kudos to Eda Zhou. She is an excellent instructor.

By Oguzcan B

Mar 29, 2021

It was very sufficient way to learn Basics of GANs for me.

By Karan S

Oct 22, 2020

It would have been nice to have the course in tensorflow.

By Samuel h

Oct 9, 2020

hope the tasks could be more challenging with more hints.

By John U

Feb 16, 2021

Great introduction to GAN's and a dive into PyTorch

By Mohamed M F

Nov 9, 2020

course needs more math, but overall it is amazing.

By Thomson T G

Feb 18, 2021

great but programming assignments felt too simple

By Joris G

Feb 7, 2021

Exercises could have been a bit more challenging.

By sanjay d

Oct 1, 2020

Course concepts gets complicates as you progress.

By Luv b

Oct 17, 2020

Good course. But still, I left with some doubts

By Rahul P

Dec 24, 2020

Best Basic Course on Generative Models.

By huaiwei c

Dec 3, 2020

need more coding exercise!!!!

By Huan T

Oct 11, 2020


By MoChuxian

Oct 13, 2020

nice !

By Marcia D R

Mar 21, 2021

El aprendizaje no ocurre desde lo más simple a lo más complejo. Simplemente se proponen videos uno después de otro sin evaluaciones formativas que efectivamente fijen el aprendizaje y sean consecuentes con la evaluación sumativa. No hay relación entre ambos tipos se evaluación ni en la dificultad que estas presentan.

En la primera tarea se evalúan aspectos que son explicados recién en la segunda unidad, ver los videos nuevamente no ayuda a entender el código que se presenta en la tarea, además se usan funciones para las que no se explica en detalle su funcionamiento.

Las lecturas paper, simplementes están linkeados en el curso, no se realiza ningún análisis de los mismos y no se elabora ninguna "bajada" del mismo que permita facilitar su comprensión. De esta manera es difícil que aporten algo al aprendizaje.

By צחי ל

Mar 4, 2021


*A lot of references to important articles.

*A lot of code in the notebooks that might be useful in the future.


*The videos lectures are not comprehensive. This is sort of "self learning" course where one should read the articles on its own in order to really understand things. This is not what I am expecting from an on-line course (and it is also not like what I got used to from the DL specialization).

*Where are the pttx? I want to print them and write some comments

*The "labs" are basically a summary of some concept. There is no added value in writing them in notebook format since the code block is just "lets load this and this, and run".

By Muhammed A Ç

Dec 5, 2020

I liked the way instructor gives lectures but one problem is unfortunately she is not explaining things widely . Another problem is programming exercises. The problem is that you cannot print your code without writing it in true way which makes really hard to debug your code. Assertion codes are not informative. And there is not a expected result info as in other courses.

By Gustavo J M

Dec 24, 2020

No se condice la pretendida profundidad de las explicaciones con las prácticas en código. Preferiría ir de a poco y más lentamente y dejar más claros los conceptos clave. La instructora es muy amable pero la velocidad del inglés es imposible de seguir para quienes no somos nativos.

By Henrik S

Apr 17, 2021

The overview of several types of GAN with their potential issues that may arise, was good.

However, I would like to see the mentors more active in the discussion groups. I still have questions, that would have been answered quite easily by the mentors. That would have been great.

By Andrea B

Dec 19, 2020

The theoretical concepts are explained in a clear way, even if I would have liked a deeper dive into the math behind the loss functions of each model proposed, moreover the assignments were too guided imo.

Nice course overall!

By Quarup B

Feb 17, 2021

Informative, but it feels like it didn't include explanations (or at least intuitions) required to fully grasp the concepts. For example, the necessity of 1L continuity and why does the enforcement work.

By Aaron S

Apr 18, 2021

Basically good, however the programming assignments are incredibly trivial compared to other machine learning courses I've taken on Coursera.