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240820s2024 sz a b 000 0 eng d |
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|a9783031549076|q(pbk.) :|cNT$1231
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|a3031549074|q(pbk.)
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|a9783031549083|q(electronic bk.)
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035 |
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|a(OCoLC)1434170992|z(OCoLC)1434095224
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040 |
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|aEBLCP|beng|eaacr|cEBLCP|dGW5XE|dYDX|dOCLCO|dTMUE
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050 |
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|aTMUE|b41|cA0341010|pB|d512.5|eZ82|y2024|tDDC|r1231
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100 |
1
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|aZizler, Peter.
|
245 |
10
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|aLinear algebra in data science /|cPeter Zizler, Roberta La Haye.
|
260 |
|
|aCham :|bBirkhäuser,|c2024
|
300 |
|
|aviii, 199 p. :|bill. ;|c24 cm.
|
490 |
1
|
|aCompact Textbooks in Mathematics
|
504 |
|
|aIncludes bibliographical references.
|
505 |
0
|
|aIntro -- Preface -- Contents -- 1 Introduction -- References -- 2 Projections -- Exercises -- References -- 3 Matrix Algebra -- Exercises -- Reference -- 4 Rotations and Quaternions -- Exercises -- References -- 5 Haar Wavelets -- Exercises -- References -- 6 Singular Value Decomposition -- Exercises -- References -- 7 Convolution -- Exercises -- References -- 8 Frequency Filtering -- Exercises -- References -- 9 Neural Networks -- References -- 10 Some Wavelet Transforms -- References -- A Appendix -- Vectors -- Exercises -- Matrices -- Exercises.
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520 |
|
|aThis 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.
|
650 |
0
|
|aAlgebras, Linear.
|
700 |
1
|
|aLa Haye, Roberta.
|
830 |
0
|
|aCompact textbooks in mathematics.
|