Before Machine Learning - Volume 1: Linear Algebra for A.I (Record no. 98108)

MARC details
000 -LEADER
fixed length control field 02358 a2200169 4500
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fixed length control field 231122b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789355424402
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.74015
Cutter BRA
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Brasil, Jorge
245 ## - TITLE STATEMENT
Title Before Machine Learning - Volume 1: Linear Algebra for A.I
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Shroff Publishers & Distributors Pvt. Ltd.
Date of publication, distribution, etc 2023
Place of publication, distribution, etc Mumbai
300 ## - PHYSICAL DESCRIPTION
Extent 164 p.
520 ## - Remark
Summary, etc Why:<br/><br/>Linear algebra is a fundamental topic for anyone working in machine learning, and it plays a critical role in understanding the inner workings of algorithms and data models. In this book, you'll learn how to apply linear algebra to real-world problems and gain a deep understanding of the concepts that drive machine learning.<br/><br/>What is different:<br/><br/>What sets this book apart is its different approach to teaching. Rather than presenting abstract mathematical concepts in isolation, the content is structured like a story with real-life examples that illustrate the practical applications of linear algebra. It is written in a conversational style as if you were having a one-on-one conversation with me, and the structure resembles a story.<br/><br/>To whom:<br/><br/>Whether you're a beginner or an experienced practitioner, this book will help you master the essentials of linear algebra and build a solid foundation for your machine-learning journey. It assumes no prior knowledge of linear algebra, making it perfect for beginners. However, it also includes advanced concepts, making it a valuable resource for more experienced learners.<br/><br/>What's inside:<br/><br/>This book covers all the essential topics in linear algebra, from vectors and matrices to eigenvalues and eigenvectors. It also includes in-depth discussions of applications of linear algebra, such as principal component analysis, and single-value decomposition.<br/><br/>Vectors addition.<br/>Multiplication of a vector by a scalar.<br/>The dot product.<br/>Vectors spaces, linear combinations, linear independence, and basis.<br/>Change of basis.<br/>Matrix and vector multiplication as well as Matrix matrix multiplication.<br/>Outer products.<br/>The inverse of a matrix.<br/>The Determinante.<br/>Systems of linear equations.<br/>Eigenvectors and eigenvalues.<br/>Eigen decomposition.<br/>The single value decomposition.<br/>The principal component analysis.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning
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 Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Programme Full call number Barcode Date last seen Cost, replacement price Koha item type
    Dewey Decimal Classification     Main Library Main Library Analytics 29/11/2023 6038 680.00   005.74015 BRA 119056 29/11/2023 850.00 Book

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