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Opiniones y comentarios de aprendices correspondientes a Fundamentals of Reinforcement Learning por parte de Universidad de Alberta

1,993 calificaciones
493 reseña

Acerca del Curso

Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general-purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization....

Principales reseñas

6 de jul. de 2020

An excellent introduction to Reinforcement Learning, accompanied by a well-organized & informative handbook. I definitely recommend this course to have a strong foundation in Reinforcement Learning.

7 de abr. de 2020

This course is one of the best I've learned so far in coursera. The explanations are clear and concise enough. It took a while for me to understand Bellman equation but when I did, it felt amazing!

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476 - 487 de 487 revisiones para Fundamentals of Reinforcement Learning

por soran q

9 de dic. de 2019

The size of different variables has not been clearly spelled out so this makes the concept confusing and requires so much time to figure them out.

por Alessandro o

14 de may. de 2020

It was quite difficult for me to follow. The concepts are explained very quickly and can be though. I found exercises very helpful though.

por MOHD F U

12 de feb. de 2020

Need a clear explanation of topics with a way to code as explained by Andrew NG in Neural networks and deep learning by

por Kun C H

29 de oct. de 2019

Explica las cosas muy por encima, no va al detalle, las prácticas un pelín difícil para gente que empieza.


27 de sep. de 2020

I automatically transferred to Auditing mode.

por Vadim A

14 de abr. de 2020

More explanations to theory would be nice.

por Jeel V

13 de jun. de 2020

More details in teaching concepts

por Simon S R

1 de sep. de 2020

They put a lot of effort into it the course, however, they choose for some reason not to share the slides with their students. The accompanying book may be the standard, but yet it does not summarize the content as the slides do.

The programming examples are to simple and to few.

A vast amount of the video contains 'what we are going to cover' and 'what we have have'. This would make sense, if there are longer videos, but not if there is just one or two minutes of content.

por Eli C

15 de sep. de 2020

the first and only other coursera course I took was mathematics of machine learning from imperial university of london. I found it challenging and educational, with fantastic presentation. it may serve as a good model to improve this course

por Pickton B

21 de jun. de 2020

Very low pedagogy in there. Just a bunch of slides (not all that good) being narrated by a standing person. You're better off reading a book.

por Amr K

25 de ene. de 2021

A Lot of theoretical math and Too few code I recommend to show this complex mathematical equetion in code also

por Jeon,Hyeon C

6 de abr. de 2021

등록 취소가 안되서 1점 드립니다.