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portada Linear Algebra for Data Science
Type
Physical Book
Language
English
Pages
256
Format
Hardcover
Dimensions
22.9 x 15.2 x 1.6 cm
Weight
0.51 kg.
ISBN13
9789811276224

Linear Algebra for Data Science

Moshe Haviv (Author) · World Scientific Publishing Company · Hardcover

Linear Algebra for Data Science - Haviv, Moshe

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Synopsis "Linear Algebra for Data Science"

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 chapters: vector algebra, linear independence and linear subspaces, orthonormal bases and the Gram-Schmidt process, linear functions. The latter comes with eight chapters: 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.

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All books in our catalog are Original.
The book is written in English.
The binding of this edition is Hardcover.

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