Practical Linear Algebra for Data Science: From Core Concepts to Applications Using Python (Record no. 98811)

MARC details
000 -LEADER
fixed length control field 02178 a2200205 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 241125b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789355422187
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.74015
Cutter COH
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Cohen Mike
245 ## - TITLE STATEMENT
Title Practical Linear Algebra for Data Science: From Core Concepts to Applications Using Python
250 ## - EDITION STATEMENT
Edition statement 1st Ed
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Shroff Publishers & Distributors Pvt. Ltd.
Date of publication, distribution, etc 2022
Place of publication, distribution, etc Mumbai
300 ## - PHYSICAL DESCRIPTION
Extent 311
520 ## - Remark
Summary, etc 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.<br/><br/>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.<br/><br/>Ideal for practitioners and students using computer technology and algorithms, this book introduces you to:<br/><br/>The interpretations and applications of vectors and matrices<br/>Matrix arithmetic (various multiplications and transformations)<br/>Independence, rank, and inverses<br/>Important decompositions used in applied linear algebra (including LU and QR)<br/>Eigendecomposition and singular value decomposition<br/>Applications including least-squares model fitting and principal components analysis.<br/><br/>Source: https://www.amazon.in/Practical-Linear-Algebra-Data-Science/dp/9355422180/ref=sr_1_1?crid=2FN6CJ81PDFLX&dib=eyJ2IjoiMSJ9.ggJB6Le-STGZGnE74Vr_-Q.vTTt5f5WSrHXBViQ4sezP_neOUHLQmLOFsv2GiLnk9c&dib_tag=se&keywords=9789355422187&qid=1732545198&sprefix=9789355420039%2Caps%2C354&sr=8-1
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Science
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Linear Algebra
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Python
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a Business Analytics
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Item type Book
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Collection Type Full call number Barcode Date last seen Cost, replacement price Koha item type
    Dewey Decimal Classification     Reference book Main Library Main Library Analytics 28/10/2024 Shroff Publishers 1160.00 Foreign Book 005.74015 COH 119579 28/10/2024 1450.00 Book

Powered by Koha