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Thompson sampling, one armed bandits, and the Beta distribution

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Thompson sampling is a strategy to explore a space while exploiting the wins. In this video we see an application to winning at a game of one-armed bandits.
Beta distributions video: • The Beta distribution ...
Tom Denton blog: inventingsitua...
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Опубликовано:

21 авг 2024

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Комментарии : 25

@marcin.sobocinski Год назад

That's the clearest and easiest to understand explanation of Thompson sampling I've ever seen! Thank you!

@arturoaltamirano1376 2 года назад

A real talent to explain complex math in simple and clear terms. Excellent use of graphics and animation.

@srinivasanbalan2469 3 года назад

You are the best teacher always, Dr. Serrano. Thanks for the upload.

@aritramandal5855 2 года назад

Such an informative video. All these complex concepts are cleared so nicely and simply.

@andrewt929 Год назад

“Best” (most intuitive, concise, and practical) explanation of both Thompson sampling and OAB I’ve found. Rivtik should be jealous.

@lenkapenka6976 2 года назад

EXCELLENT Video! And one of the few that explain the Bandit problem clearly and succinctly with Thompson sampling.

@rng1160 3 года назад

another great video, really appreciate the simplicity and knowledge in it

@JackLiu-xn4jz 7 месяцев назад

Because there's no Chinese subtitle in the video so I would like use English to reply the comment。 the video is cool and easy to understand and I understand it and benefit from it lot. Thank you, thank you very much

@priyasearcher 3 года назад

Thank u so much sir... Want more videos on machine learning and deep learning topics

@abdealiarsiwala5485 5 месяцев назад

Amazing explaination!

@etienneboutet7193 3 года назад

Thank you for this video. Very informative as always !

@busn311 2 года назад

8:23 an error in the third graph when adding the third trial. The graph should move skew to the right instead of left.

@SerranoAcademy Год назад

You’re right, thank you for the correction!

@ullibowyer 5 месяцев назад

9:17 you say "picking a random point from the distribution" which makes it seem like the x coordinate is randomly chosen. I think it's much clearer to say draw a random sample from the distribution.