Haviv M Linear Algebra for Data Science 2023
Category: eBooks
- views: 190
- date: 9 August 2023
- posted by: BeMyLove
Haviv M Linear Algebra for Data Science 2023 | 10.5 MB
N/A | 257 Pages
Title: Linear Algebra for Data Science (256 Pages)
Author: Moshe Haviv
Year: 2023
Description:
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 applications to data science along the way.The book comes with two parts, one on vectors, the other on matrices. The former consists of four vector algebra, linear independence and linear subspaces, orthonormal bases and the Gram-Schmidt process, linear functions. The latter comes with eight matrices and matrix operations, invertible matrices and matrix inversion, projections and regression, determinants, eigensystems and diagonalizability, symmetric matrices, singular value decomposition, and stochastic matrices. The book ends with the solution of exercises which appear throughout its twelve chapters.
DOWNLOAD:
https://rapidgator.net/file/d11b06a3d83bad9ff77a3db41f752c8a/Haviv_M._Linear_Algebra_for_Data_Science_2023.rar
https://k2s.cc/file/5b8311dac5ef0/Haviv_M._Linear_Algebra_for_Data_Science_2023.rar
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 applications to data science along the way.The book comes with two parts, one on vectors, the other on matrices. The former consists of four vector algebra, linear independence and linear subspaces, orthonormal bases and the Gram-Schmidt process, linear functions. The latter comes with eight matrices and matrix operations, invertible matrices and matrix inversion, projections and regression, determinants, eigensystems and diagonalizability, symmetric matrices, singular value decomposition, and stochastic matrices. The book ends with the solution of exercises which appear throughout its twelve chapters.
DOWNLOAD:
https://rapidgator.net/file/d11b06a3d83bad9ff77a3db41f752c8a/Haviv_M._Linear_Algebra_for_Data_Science_2023.rar
https://k2s.cc/file/5b8311dac5ef0/Haviv_M._Linear_Algebra_for_Data_Science_2023.rar
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