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Information Theoretic Principles for Agent Learning
Overview
This book provides readers with the fundamentals of information theoretic techniques for statistical data science analyses and for characterizing the behavior and performance of a learning agent outside of the standard results on communications and compression fundamental limits. Readers will benefit from the presentation of information theoretic quantities, definitions, and results that provide or could provide insights into data science and learning.
This item is Non-Returnable
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Details
- ISBN-13: 9783031653872
- ISBN-10: 3031653874
- Publisher: Springer
- Publish Date: August 2024
- Dimensions: 9.3 x 6.7 x 0.5 inches
- Shipping Weight: 0.75 pounds
- Page Count: 95
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