Information Theoretic Principles for Agent Learning
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Beschreibung
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.
In addition, this book:
- Describes the fundamentals of information theoretic techniques for statistical data science analyses
- Provides succinct introductions to key topics, with references as needed for further technical depth
- Enables readers from varying backgrounds to understand the behavior and performance of a learning agent
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.
Describes the fundamentals of information theoretic techniques for statistical data science analyses Provides succinct introductions to key topics, with references as needed for further technical depth Enables readers from varying backgrounds to understand the behavior and performance of a learning agent
Autor*in
Jerry D. Gibson
Themen in »Information Theoretic Principles for Agent Learning«
Information theory Learning agent Deep learning Entropy and mutual information for discrete random variables Asymptotic equipartition