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Linear algebra and learning from data

Linear algebra and learning from data (Loan 17 times)

Material type
단행본
Personal Author
Strang, Gilbert.
Title Statement
Linear algebra and learning from data / Gilbert Strang.
Publication, Distribution, etc
Wellesley, MA :   Wellesley-Cambridge Press,   c2019.  
Physical Medium
xiii, 432 p. : ill. ; 25 cm.
ISBN
9780692196380
General Note
Includes index.  
Subject Added Entry-Topical Term
Algebras, Linear --Textbooks. Mathematical optimization --Textbooks. Mathematical statistics --Textbooks. Algebra lineal.
000 00000nam u2200205 a 4500
001 000045980440
005 20191031141015
008 190411s2019 maua 001 0 eng d
020 ▼a 9780692196380
040 ▼a 211009 ▼c 211009 ▼d 211009
082 ▼a 512.5 ▼2 23
084 ▼a 512.5 ▼2 DDCK
090 ▼a 512.5 ▼b S897Ln
100 1 ▼a Strang, Gilbert.
245 1 0 ▼a Linear algebra and learning from data / ▼c Gilbert Strang.
260 ▼a Wellesley, MA : ▼b Wellesley-Cambridge Press, ▼c c2019.
300 ▼a xiii, 432 p. : ▼b ill. ; ▼c 25 cm.
500 ▼a Includes index.
650 0 ▼a Algebras, Linear ▼v Textbooks.
650 0 ▼a Mathematical optimization ▼v Textbooks.
650 0 ▼a Mathematical statistics ▼v Textbooks.
650 0 ▼a Algebra lineal.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 512.5 S897Ln Accession No. 121248604 Availability Available Due Date Make a Reservation Service B M
No. 2 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 512.5 S897Ln Accession No. 121250786 Availability Available Due Date Make a Reservation Service B M

Contents information

Author Introduction

Gilbert Strang(지은이)

매사추세츠공과대학교(MIT) 수학과 교수이자 응용수학의 대가입니다. MIT에서 학사를 졸업한 후 영국 옥스퍼드 대학교에서 석사 학위를, UCLA에서 박사 학위를 받았습니다. 그의 주요 연구 분야는 유한요소이론, 변분법, 웨이블릿 분석, 선형대수학입니다. 주요 저서로는 『Linear Algebra and Learning form Data(2019)』, 『Calculus, 3rd edition(2017)』, 『Introduction to Linear Algebra, 5th edition(2016)』, 『Essay in Linear Algebra(2012)』 등이 있습니다.

Information Provided By: : Aladin

Table of Contents

Deep learning and neural nets
Preface and acknowledgements
Part I: Highlights of linear algebra
Part II: Computations with large matrices
Part III: Low rank and compressed sensing
Part IV: Special matrices
Part V: Probability and statistics
Part IV: Optimization
Part VII: Learning from data
Books on machine learning
Eigenvalues and singular values : rank one
Codes and algorithms for numerical linear algebra
Counting parameters in the basic factorizations
Index of authors
Index
Index of symbols.

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