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.
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.
By Yudun W•
A very easy to understand guide for those who are interested in how GAN generally works!
By Nicola P•
Exceptional theoretical part, but mandatory assignments are way too simple
By venu v•
More help (and annotations) on the code beyond start/end blocks would help
By Niraj S•
Loving it so far. Kudos to Eda Zhou. She is an excellent instructor.
By Oguzcan B•
It was very sufficient way to learn Basics of GANs for me.
By Karan S•
It would have been nice to have the course in tensorflow.
By Samuel h•
hope the tasks could be more challenging with more hints.
By John U•
Great introduction to GAN's and a dive into PyTorch
By Mohamed M F•
course needs more math, but overall it is amazing.
By Thomson T G•
great but programming assignments felt too simple
By Joris G•
Exercises could have been a bit more challenging.
By sanjay d•
Course concepts gets complicates as you progress.
By Luv b•
Good course. But still, I left with some doubts
By Rahul P•
Best Basic Course on Generative Models.
By huaiwei c•
need more coding exercise!!!!
By Huan T•
By Marcia D R•
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•
*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•
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•
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•
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•
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•
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•
Basically good, however the programming assignments are incredibly trivial compared to other machine learning courses I've taken on Coursera.