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Deep Learning Architectures : A Mathematical Approach / by Ovidiu Calin
(Springer Series in the Data Sciences. ISSN:23655682)
版 | 1st ed. 2020. |
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出版者 | Cham : Springer International Publishing : Imprint: Springer |
出版年 | 2020 |
本文言語 | 英語 |
大きさ | XXX, 760 p. 207 illus., 35 illus. in color : online resource |
著者標目 | *Calin, Ovidiu author SpringerLink (Online service) |
件 名 | LCSH:Computer science -- Mathematics
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LCSH:Machine learning FREE:Mathematical Applications in Computer Science FREE:Machine Learning |
一般注記 | Introductory Problems -- Activation Functions -- Cost Functions -- Finding Minima Algorithms -- Abstract Neurons -- Neural Networks -- Approximation Theorems -- Learning with One-dimensional Inputs -- Universal Approximators -- Exact Learning -- Information Representation -- Information Capacity Assessment -- Output Manifolds -- Neuromanifolds -- Pooling -- Convolutional Networks -- Recurrent Neural Networks -- Classification -- Generative Models -- Stochastic Networks -- Hints and Solutions. 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. HTTP:URL=https://doi.org/10.1007/978-3-030-36721-3 |
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電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
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電子ブック | オンライン | 電子ブック |
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Springer eBooks | 9783030367213 |
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EB00226353 |
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データ種別 | 電子ブック |
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分 類 | LCC:QA76.9.M35 DC23:004.0151 |
書誌ID | 4000134773 |
ISBN | 9783030367213 |
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