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I had a background in undergrad Linear Algebra from one of the Strang's book "Introduction to Linear Algebra" before reading this book. Even with that background, I haven't been
able to make sense of it, even after watching the accompanying lectures. The basic problem is Strang loves to introduce an idea without explaining it, perhaps for motivation or to give readers the sneak peek to readers of the wonderful things that await them. In this book, however, he has taken it too far. It's just one i The basic problem is Strang loves to introduce an idea without explaining it, perhaps for motivation or to give readers the sneak peek to readers of the wonderful things that await them. In this book, however, he has taken it too far. It's just one idea after another without any explanation, why is the idea useful, and where is it coming from? In the end, I think the
struggle to make sense of this book is not worth it.
William Gilbert Strang (born November 27, 1934), usually known as simply Gilbert Strang or Gil Strang, is an American mathematician, with contributions to finite element theory, the calculus of variations, wavelet analysis and linear algebra. He has made many contributions to mathematics education, including publishing seven mathematics textbooks and one monograph. Strang is the MathWorks Professo William Gilbert Strang (born November 27, 1934), usually known as simply Gilbert Strang or Gil Strang, is an American mathematician, with contributions to finite element theory, the calculus of variations, wavelet analysis and linear algebra. He has made many contributions to mathematics education, including publishing seven mathematics textbooks and one monograph. Strang is the MathWorks Professor of Mathematics at the Massachusetts Institute of Technology. He teaches Introduction to Linear Algebra and Computational Science and Engineering and his lectures are freely available through MIT OpenCourseWare. ...more Related ArticlesSummer is winding down, but that’s OK, as long as there are good books to read. As it happens, there are some great new books coming in... Welcome back. Just a moment while we sign you in to your Goodreads account. How is linear algebra used in machine learning?To learn and understand Machine Learning or Data Science, one needs to be familiar with linear algebra and optimization theory.. Datasets and Data Files. ... . Images and Photographs. ... . One Hot Encoding. ... . Linear Regression. ... . Regularization. ... . Principal Component Analysis. ... . Singular-Value Decomposition.. What is linear algebra in data science?Linear algebra becomes the study of the basic operation of linear combination and its potential as a descriptor of large data sets. Rather than concentrate on the basis transformation represented by linear system solution, the focus shiſts to maximal information compression.
Who discovered linear algebra?Systems of linear equations arose in Europe with the introduction in 1637 by René Descartes of coordinates in geometry. In fact, in this new geometry, now called Cartesian geometry, lines and planes are represented by linear equations, and computing their intersections amounts to solving systems of linear equations.
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Gilbert strang linear algebra and learning from data
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