| 书目名称 | Information Theoretic Principles for Agent Learning |
| 编辑 | Jerry D. Gibson |
| 视频video | |
| 概述 | 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 dept |
| 丛书名称 | Synthesis Lectures on Engineering, Science, and Technology |
| 图书封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2025 |
| 关键词 | Information theory; Learning agent; Deep learning; Entropy and mutual information for discrete random v |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-65388-9 |
| isbn_softcover | 978-3-031-65390-2 |
| isbn_ebook | 978-3-031-65388-9Series ISSN 2690-0300 Series E-ISSN 2690-0327 |
| issn_series | 2690-0300 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
发表于 2025-3-21 19:14:18
