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

4.7
stars
1,111 ratings
277 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.

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251 - 275 of 279 Reviews for Build Basic Generative Adversarial Networks (GANs)

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

wunderbar

By MoChuxian

Oct 13, 2020

nice !

By Vikram N

May 26, 2021

The course started well but went downhill in week 3. The videos, actually get shorter and the treatment provided to the material related to Wasserstein distance, 1-L Continuity, interpolation and other crucial topics is just superficial. There are not adequate number of quizzes to test yourself. There is insufficient mathematical rigour. And it is too easy to pass the graded assignments without actually understanding the material. The forums are somewhat dead and you need to go to the Slack rooms to ask questions. On slack, it is a case of people linking to other papers rather than providing simple, direct answers. Nobody knows anything for sure. Overall, there is a take-it or leave-it attitude in this course and it is a far cry from Andrew's original ML Course which made Coursera such an attractive learning destination. I do hope the course is improved over time by adding more quizzes, delving deeper into topics (it's okay to have long videos where the instructor explains things slowly) and providing a more mathematically satisfying experience where the foundations are made stronger.

On the positive aspects - the notebooks provided are an excellent starting point to begin your own explorations. And the material is cutting edge.

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

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 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 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.