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Linear Algebra in Data Science / by Peter Zizler, Roberta La Haye.
Springer Nature - Springer Mathematics and Statistics eBooks 2024 English International Available online
View online- Format:
- Book
- Author/Creator:
- Zizler, Peter.
- Series:
- Compact Textbooks in Mathematics, 2296-455X
- Language:
- English
- Subjects (All):
- Algebras, Linear.
- Artificial intelligence--Data processing.
- Artificial intelligence.
- Computer science--Mathematics.
- Computer science.
- Linear Algebra.
- Data Science.
- Mathematical Applications in Computer Science.
- Local Subjects:
- Linear Algebra.
- Data Science.
- Mathematical Applications in Computer Science.
- Physical Description:
- 1 online resource (202 pages)
- Edition:
- 1st ed. 2024.
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Birkhäuser, 2024.
- Summary:
- 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.
- Contents:
- Intro
- Preface
- Contents
- 1 Introduction
- References
- 2 Projections
- Exercises
- 3 Matrix Algebra
- Reference
- 4 Rotations and Quaternions
- 5 Haar Wavelets
- 6 Singular Value Decomposition
- 7 Convolution
- 8 Frequency Filtering
- 9 Neural Networks
- 10 Some Wavelet Transforms
- A Appendix
- Vectors
- Matrices
- Exercises.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-031-54908-2
- OCLC:
- 1435751398
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