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Linear algebra in data science

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Linear algebra in data science/ by Peter Zizler, Roberta La Haye.
作者:	Zizler, Peter.
其他作者:	La Haye, Roberta.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	viii, 199 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Algebras, Linear. -
電子資源:	https://doi.org/10.1007/978-3-031-54908-3
ISBN:	9783031549083

Linear algebra in data science
Zizler, Peter.

Linear algebra in data science[electronic resource] /by Peter Zizler, Roberta La Haye. - Cham :Springer International Publishing :2024. - viii, 199 p. :ill., digital ;24 cm. - Compact textbooks in mathematics,2296-455X. - Compact textbooks in mathematics..

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.

ISBN: 9783031549083

Standard No.: 10.1007/978-3-031-54908-3doiSubjects--Topical Terms:

521915
Algebras, Linear.

LC Class. No.: QA184.2

Dewey Class. No.: 512.5

Linear algebra in data science
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520 $a 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.
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