Linear Algebra in Data Science
β Scribed by Peter Zizler, Roberta La Haye
- Publisher
- BirkhΓ€user
- Year
- 2024
- Tongue
- English
- Leaves
- 203
- Series
- Compact Textbooks in Mathematics
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Table of Contents
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
π SIMILAR VOLUMES
<span>This book serves as an introduction to linear algebra for undergraduate students in data science, statistics, computer science, economics, and engineering. The book presents all the essentials in rigorous (proof-based) manner, describes the intuition behind the results, while discussing some a
<span>This book serves as an introduction to linear algebra for undergraduate students in data science, statistics, computer science, economics, and engineering. The book presents all the essentials in rigorous (proof-based) manner, describes the intuition behind the results, while discussing some a
If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades