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 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 Vikram N•
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•
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 Abhik L•
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•
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.
be unfamiliar with english and unlike Andrew use mathematical formula ， so i learn a little hard
By Michael K•
Great intuitive explanations but it is too easy
By Christoffer M•
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•
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.