Opiniones y comentarios de aprendices correspondientes a Sample-based Learning Methods por parte de Universidad de Alberta
Acerca del Curso
Principales reseñas
DP
14 de feb. de 2021
Excellent course that naturally extends the first specialization course. The application examples in programming are very good and I loved how RL gets closer and closer to how a living being thinks.
AA
11 de ago. de 2020
Great course, giving it 5 stars though it deserves both because the assignments have some serious issues that shouldn't actually be a matter. All the other parts are amazing though. Good job
76 - 100 de 212 revisiones para Sample-based Learning Methods
por Maryam T
•16 de nov. de 2021
A very good course for understanding basic concepts of RL. It is not enough for doing projects with coding.
por Stewart A
•3 de sep. de 2019
Great course! Lots of hands-on RL algorithms. I'm looking forward to the next course in the specialization.
por Casey S S
•11 de feb. de 2021
I thought this was an excellent sequel, introducing the reader to the fundamental innovations of RL.
por Martin P
•30 de may. de 2020
A very interesting topic presented in an easy to consume form. It was fun learning with this course.
por 석박통합김한준
•7 de abr. de 2020
The course is spectacular! I've learned countless material on Reinforcement learning! Thank you!
por Roberto M
•28 de mar. de 2020
The course is well organized and teachers provide a lot of examples to facilitate comprehension.
por Chintan K
•22 de jul. de 2020
the course videos were short and precise , which makes the learning content fun and informative
por Wang G
•19 de oct. de 2019
Very Nice Explanation and Assignment! Look forward the next 2 courses in this specialization!
por Sodagreenmario
•18 de sep. de 2019
Great course, but there are still some little bugs that can be fixed in notebook assignments.
por Floris v R
•4 de ene. de 2022
Very clear explanations in the videos, good tests & asignments. Complex stuff well explained
por Chris D
•18 de abr. de 2020
Very good. Minor issues with inconsistency between parameter naming in different exercises.
por Sirusala N S
•30 de jul. de 2020
The concepts were explained very clearly. The assignments were helpful in understanding.
por koji t
•6 de oct. de 2019
I made a lot of mistakes, but I learned a lot because of that.
It ’s a wonderful course.
por Sérgio V C
•15 de mar. de 2021
A good course to learn the basics of Monte Carlo methods for RL, as well as TD-methods!
por Jau-Jie Y
•7 de jul. de 2021
I am happy of the history talking of Barto and Sutton.
The others teachers were good.
por Louis S
•5 de jun. de 2020
Excellent content. The fact that it follows Sutton and Barto's TextBook is a must.
por Pruthvi J
•7 de feb. de 2021
Excellent course, gives a decent theoretical and practical introduction to RL.
por Corey A
•19 de abr. de 2022
Awesome course. Fun examples and exercises and great compliment to the book.
por ding l
•24 de abr. de 2020
By taking the class, I learned much more than only reading the textbook.
por Ofir E
•22 de mar. de 2020
Amazing course, truly academy-grade. And RL is such a fascinating topic!
por Fabrice L
•14 de nov. de 2020
Things start to get interesting in this course of the specialization.
por Deleted A
•10 de mar. de 2020
It was a very good course. All the concepts were explained very well.
por leftheory
•6 de jul. de 2021
Very neat and clear. However, it just follows the RL book strictly.
por Varun K R K
•15 de may. de 2021
The best course available on entire world for reinforcement learning
por Dan N
•24 de oct. de 2021
I liked that this course had programming assignments for each week.
Coursera Footer
