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Linear Algebra in Data Science / by Peter Zizler, Roberta La Haye
(Compact Textbooks in Mathematics. ISSN:2296455X)
版 | 1st ed. 2024. |
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出版者 | Cham : Springer International Publishing : Imprint: Birkhäuser |
出版年 | 2024 |
本文言語 | 英語 |
大きさ | VIII, 199 p. 23 illus., 9 illus. in color : online resource |
著者標目 | *Zizler, Peter author La Haye, Roberta author SpringerLink (Online service) |
件 名 | LCSH:Algebras, Linear LCSH:Artificial intelligence -- Data processing 全ての件名で検索 LCSH:Computer science -- Mathematics 全ての件名で検索 FREE:Linear Algebra FREE:Data Science FREE:Mathematical Applications in Computer Science |
一般注記 | This textbook explores applications of linear algebra in data science at an introductory level, showing readers how the two are deeply connected. The authors accomplish this by offering exercises that escalate in complexity, many of which incorporate MATLAB. Practice projects appear as well for students to better understand the real-world applications of the material covered in a standard linear algebra course. Some topics covered include singular value decomposition, convolution, frequency filtering, and neural networks. Linear Algebra in Data Science is suitable as a supplement to a standard linear algebra course HTTP:URL=https://doi.org/10.1007/978-3-031-54908-3 |
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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 | 9783031549083 |
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電子リソース |
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EB00240163 |