A Profit-Maximizing Reinforcement Learning-Based AI System in Python

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Dr. Soper provides a complete example of a profit-maximizing artificial intelligence system in Python that relies on Thompson Sampling-based reinforcement learning to decide which advertising campaign to show to a company's customers. Each part of the Python source code and the corresponding results are discussed in detail to ensure understanding.

  1. Dr. Daniel Soper
  2. artificial intelligence
  3. AI
  4. Python
  5. reinforcement learning
  6. thompson sampling
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Комментарии
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Best content ever on reinforcement learning

saeedseyedhossein
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Thank you. Short videos with sufficient explanation.

FatemehBagherpour-vu
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great example and explanation. Thanks!

fabianlopez
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Another super video. I am new to this topic. Don't me asking where is the data related to Advertising Campaigns? Thanks.

EustaquioSantimano
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The choice of the b parameter for the Beta distribution was very insightful. Any additional resource you recommend for that topic?

seydoudia
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In practice, true probabilities of campaign success are not known.what changes to the code should we make in that case for the information the mobile company does not have?

tomjoseph
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I'm no expert at this stuff, but I suspect that a better way of choosing which campaign to try _might_ be to use a Gamma distribution (allows score-like values in [0-infinity) ) rather than a Beta distribution contorted to handle values that aren't counts or probabilities. The Gamma dist is very closely related to the Beta, hence why I suspect it might be the one to try.

robharwood
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