Book description
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 decadesold textbooks is much different from how professionals use linear algebra today to solve realworld modern applications.
This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms.
Ideal for practitioners and students using computer technology and algorithms, this book introduces you to:
 The interpretations and applications of vectors and matrices
 Matrix arithmetic (various multiplications and transformations)
 Independence, rank, and inverses
 Important decompositions used in applied linear algebra (including LU and QR)
 Eigendecomposition and singular value decomposition
 Applications including leastsquares model fitting and principal components analysis
Publisher resources
Table of contents
 Preface
 1. Introduction
 2. Vectors, Part 1
 3. Vectors, Part 2
 4. Vector Applications
 5. Matrices, Part 1
 6. Matrices, Part 2
 7. Matrix Applications
 8. Matrix Inverse
 9. Orthogonal Matrices and QR Decomposition
 10. Row Reduction and LU Decomposition
 11. General Linear Models and Least Squares
 12. Least Squares Applications

13. Eigendecomposition
 Interpretations of Eigenvalues and Eigenvectors
 Finding Eigenvalues
 Finding Eigenvectors
 Diagonalizing a Square Matrix
 The Special Awesomeness of Symmetric Matrices
 Eigendecomposition of Singular Matrices
 Quadratic Form, Definiteness, and Eigenvalues
 Generalized Eigendecomposition
 Summary
 Code Exercises
 14. Singular Value Decomposition
 15. Eigendecomposition and SVD Applications
 16. Python Tutorial
 Index
 About the Author
Product information
 Title: Practical Linear Algebra for Data Science
 Author(s):
 Release date: September 2022
 Publisher(s): O'Reilly Media, Inc.
 ISBN: 9781098120610
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