Chevron Left
Вернуться к Practical Reinforcement Learning

Отзывы учащихся о курсе Practical Reinforcement Learning от партнера Национальный исследовательский университет "Высшая школа экономики"

4.2
звезд
Оценки: 394
Рецензии: 111

О курсе

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...

Лучшие рецензии

AK

May 28, 2019

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

Apr 09, 2020

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.

Фильтр по:

101–111 из 111 отзывов о курсе Practical Reinforcement Learning

автор: Xiaoahe X

Feb 20, 2019

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.

автор: Helmut G

Aug 22, 2018

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.

автор: Felix A

Mar 18, 2019

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)

автор: Ishan

Jul 09, 2020

Lectures are mathematical and theoretical.

Assignments are practical. NO EXPLANATION OF WHAT ASSIGNMENTS IN CLASS

автор: Dai T

Jul 29, 2018

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

автор: Sylvain D

Dec 03, 2019

Good

автор: Anders A

Jun 13, 2018

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.

автор: Maxim B

Jul 26, 2018

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.

автор: Raghu R

Mar 25, 2020

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..

автор: Arun A

Feb 24, 2020

Instructors are difficult to understand. Assignment directions are not clear

автор: Antony L

Mar 12, 2019

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.