9781733146630-1733146636-Linear Algebra for Everyone (The Gilbert Strang Series)

Linear Algebra for Everyone (The Gilbert Strang Series)

ISBN-13: 9781733146630
ISBN-10: 1733146636
Edition: New
Author: Gilbert Strang
Publication date: 2020
Publisher: Wellesley-Cambridge Press
Format: Hardcover 368 pages
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ISBN-13: 9781733146630
ISBN-10: 1733146636
Edition: New
Author: Gilbert Strang
Publication date: 2020
Publisher: Wellesley-Cambridge Press
Format: Hardcover 368 pages

Summary

Linear Algebra for Everyone (The Gilbert Strang Series) (ISBN-13: 9781733146630 and ISBN-10: 1733146636), written by authors Gilbert Strang, was published by Wellesley-Cambridge Press in 2020. With an overall rating of 3.7 stars, it's a notable title among other Pure Mathematics (Mathematics) books. You can easily purchase or rent Linear Algebra for Everyone (The Gilbert Strang Series) (Hardcover, Used) from BooksRun, along with many other new and used Pure Mathematics books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $27.89.

Description

Linear algebra has become the subject to know for people in quantitative disciplines of all kinds. No longer the exclusive domain of mathematicians and engineers, it is now used everywhere there is data and everybody who works with data needs to know more. This new book from Professor Gilbert Strang, author of the acclaimed Introduction to Linear Algebra, now in its fifth edition, makes linear algebra accessible to everybody, not just those with a strong background in mathematics. It takes a more active start, beginning by finding independent columns of small matrices, leading to the key concepts of linear combinations and rank and column space. From there it passes on to the classical topics of solving linear equations, orthogonality, linear transformations and subspaces, all clearly explained with many examples and exercises. The last major topics are eigenvalues and the important singular value decomposition, illustrated with applications to differential equations and image compression. A final optional chapter explores the ideas behind deep learning.

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