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商品詳細
Deep Learning Architectures: A Mathematical Approach. 1st ed. 2020
・ISBN 978-3-030-36720-6 hard EUR 89.99
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| 著者・編者 | Calin, Ovidiu, |
|---|---|
| シリーズ | (Springer Series in the Data Sciences) |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2020 |
| ページ数 | 760 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A02-47403＞ |
解説
This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter.
This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.