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Practical Linear Algebra for Data Science: From Core Concepts to Applications

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Item specifics

Condition
Very Good: A book that has been read and does not look new, but is in excellent condition. No ...
Brand
Unbranded
Book Title
Practical Linear Algebra for Data Science: From Core Concepts to
MPN
Does not apply
ISBN
9781098120610
Category

About this product

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1098120612
ISBN-13
9781098120610
eBay Product ID (ePID)
16057246179

Product Key Features

Number of Pages
326 Pages
Publication Name
Practical Linear Algebra for Data Science : from Core concepts to Applications Using Python
Language
English
Subject
Algebra / Linear, Data Processing
Publication Year
2022
Type
Textbook
Subject Area
Mathematics, Computers
Author
Mike X. Cohen
Format
Trade Paperback

Dimensions

Item Height
0.9 in
Item Weight
19.9 Oz
Item Length
9.1 in
Item Width
7 in

Additional Product Features

Intended Audience
Trade
LCCN
2022-301984
Illustrated
Yes
Synopsis
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-old textbooks is much different from how professionals use linear algebra today to solve real-world 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 least-squares model fitting and principal components analysis
LC Classification Number
QA185.D37C64 2022

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ANJ Imports

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