Linear Algebra and Learning from Data (2019)

by Gilbert Strang   (gilstrang@gmail.com)     ISBN : 978-06921963-8-0

Wellesley-Cambridge Press

Book Order from Wellesley-Cambridge Press
Book Order for SIAM members

Book Order from American Mathematical Society

Book Order from Cambridge University Press (outside North America)

Linear Algebra and Learning from Data, Indian edition, is available at Wellesley Publishers

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Contents, selected sections, essays, reviews, errata

• SIAM Review (2020)
• Book review from insideBIGDATA (2020)
• Bibliography: Printed publications and online references
• The Functions of Deep Learning (essay from SIAM News, December 2018)
• Deep Learning and Neural Nets
• Preface
• Table of Contents
• I.1 - Multiplication of Ax Using Columns of A
• I.2 - Matrix-Matrix Multiplication AB
• VII.1 - The Construction of Deep Neural Networks
• Counting Parameters in the Basic Factorizations
• Matrix derivatives by Dan Drucker : Proof that A_k is the closest rank k matrix to A
• The Central Limit Theorem (page 288)
• Convolution as a Moving Window
• Errata in the First Printing

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Solutions to Problems at the End of the Chapters

• Selected solutions by the Author (November 2023)
• Selected solutions by Elizabeth Chang-Davidson (21 August 2019)
• Selected solutions by Tony Tohme (21 August 2019)

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Other books by Gilbert Strang

• Linear Algebra for Everyone (new textbook, September 2020)   SEE NOTE BELOW
• Introduction to Linear Algebra
• Differential Equations and Linear Algebra
• Computational Science and Engineering
• Calculus

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Our recent textbook Linear Algebra for Everyone starts with the idea of independent columns

    This leads to a factorization A = CR where C contains those independent columns from A

    The matrix R tells how to combine those columns of C to produce all columns of A

    Then Section 3.2 explains how to solve Rx = 0. This gives the nullspace of A  !!

Here is that new section : A = CR and Computing the Nullspace by Elimination

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