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ロシア国立研究大学経済高等学院(National Research University Higher School of Economics) による Practical Reinforcement Learning の受講者のレビューおよびフィードバック

4.2
405件の評価
111件のレビュー

コースについて

Welcome to the Reinforcement Learning course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. --- with math & batteries included - using deep neural networks for RL tasks --- also known as "the hype train" - state of the art RL algorithms --- and how to apply duct tape to them for practical problems. - and, of course, teaching your neural network to play games --- because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits. Jump in. It's gonna be fun! Do you have technical problems? Write to us: coursera@hse.ru...

人気のレビュー

AK
2019年5月27日

This is one of the Best Course available on Reinforcement Learning. I have gone through various study material but the depth and practical knowledge given in the course is awesome.

SF
2020年4月8日

At times it felt like a bit more video material would be helpful to better understand the subject/gain deeper understanding.\n\nAnd fixing some of the notebooks would be helpful.

フィルター:

Practical Reinforcement Learning: 101 - 111 / 111 レビュー

by Xiaoahe X

2019年2月20日

The course is well organized. Reference and extra learning items is helpful to enhance the knowledge.

BUT! There are so many small bugs in the assignments that it really takes time to fix and make the course hard to get passed.

by Helmut G

2018年8月22日

Sometimes it is hard to understand/follow the instructors. And the assignments (especially the grader) are bit too much beta, which causes a lot of extra effort.

by Felix A

2019年3月18日

The course itself is great, but the assignments are a bit chaotic (so make sure to bring a lot of patience and willingness to bugfix)

by Ishan

2020年7月9日

Lectures are mathematical and theoretical.

Assignments are practical. NO EXPLANATION OF WHAT ASSIGNMENTS IN CLASS

by Dai T

2018年7月29日

Lots of theory and definition should be illustrate in detail on ppt.

by Sylvain D

2019年12月3日

Good

by Anders A

2018年6月13日

The collection of curriculum was great together with links to external resources. However, there was several weaknesses with the course. First, several of the assignments had problems with submitting the code, which required some extra coding to be able to submit assignment. Event with multiple weeks with many students reporting the problem, nothing was corrected. Second, the lectures were weak. I knew something about Q-learning from before, but after the lecture I was more confused than when I started. The topics I were not familiar from before the course,I ended up searching online or using the resources linked to instead of the lectures. The question exercises felt arbitrary and not helpful at all. The programming exercises were not well explained. I were able to finish them, but to much unnecessary annoyance.

by Maxim B

2018年7月26日

Don't let Alexander Panin read lectures. He is an awful speaker: always in a hurry, uses so many redundant words in his speech. He "killed" so much interesting material in this course. I truly believe he could write cool lecture notes and handouts (currently the course lacks it). Alexander, please, write materials, don't read lectures.

by Raghu R

2020年3月25日

Course is good. But too many grader issues. Accent is tough to understand sometimes. The concept is not built layer wise..Instead they dump it as a heap with tough jargon which had to broken down to be understood slowly by pausing..

by Arun A

2020年2月23日

Instructors are difficult to understand. Assignment directions are not clear

by Antony L

2019年3月12日

Course not ready and has installation prerequisites. Seems to use a libraries (Docker, Env).

I waste too much of my time trying to install libraries and dependencies for online courses, most of which become obsolete within a year or two.

Additionally, the logic embedded within the library is often the thing I want to learn, and abstracting it only teaches me about the bugs and shortcomings of that library.