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
1,170 ratings
291 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

MS
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.

DP
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.

Filter by:

276 - 294 of 294 Reviews for Build Basic Generative Adversarial Networks (GANs)

By צחי ל

Mar 4, 2021

Pros:

*A lot of references to important articles.

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

Cons:

*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 Alberto G

Jul 13, 2021

Under resourced course. Instructors do not reply to questions. If you have problems with the application, you are alone with very poor support and not clear reponsibilities on who can help you. Very disappointing and frustrating. Not practical information on how to deal with custom datasets provided. It is just a tutorial with an "easy" example.

By Abhik L

Jun 14, 2021

The course was a good high-level introduction to GANs. The lectures were clear and very well done, however the course lacked mathematical rigor. The in-lecture quizzes were trivial, and so were the programming assignments. This course in isolation is not sufficient to get you started with GANs in the real world.

By Gustavo 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 Naveed M

Jul 1, 2021

The programming assignments can be improved by designing it in such a way that most of the work should be done learner not by the course designer. I hope you change it in future.

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.

By YutaoLAN

Oct 9, 2020

be unfamiliar with english and unlike Andrew use mathematical formula , so i learn a little hard

By Michael K

Oct 12, 2020

Great intuitive explanations but it is too easy

By Christoffer M

Mar 4, 2021

The GANs in the course are basic as advertised, but unfortunately the treatment of the theory is basic and shallow as well. The lab assignments are too simplistic to force any deeper understanding.

By Fatemeh A

Jun 11, 2021

It was too high level without mentioning the math behind the theories. The codes were too simple and not challenging. The instructor was speaking too fast.

By Yu G

Jan 17, 2021

Homework size are TOO large! One star given. One additional for that this course is highly challenging.

By Daniel J

Feb 27, 2021

The content is clear but lacks any real depth. Any time a more difficult topic pops up the details are completely ignored or swept under the rug without any acknowledgement. Even a comment like "this topic is beyond the scope of what we want to cover here, go to this resource to learn more..." would have been far preferable. This seems to be a recurring theme in recent specialisations by deeplearning.ai rather than the fault of this particular instructor.

By Ranga R S

Feb 11, 2021

Had to pause multiple times to listen again or read the English translation at the bottom. Slowing down the lecture along with proper pauses and meaningful visual illustrations can improve this course in a big way.

Content of this course is good, but the way it is presented leaves much to be desired

By Michael S

Feb 7, 2021

The coding exercises seem completely unguided by the course, and feel like a waste of my time.

I'm not going to pay you for the time I spend studying pytorch.org

By David S C A

Jul 20, 2021

Way, way, way too light on the details