Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares 1st Edition

This is a great book for what the book advertises to do - applied linear algebra. There isn't a ton of theory in the book, but some key theoretical points to understand are covered in the book, along with simple proofs to go along with those theoretical foundations. Where the book really shines is giving you an idea of the numerous applications and flexibility of linear algebra. I've gone through almost 11 chapters and am finding this a really valuable read. However, if you're looking for a more theoretical read, you probably would want to supplement this with another book on elementary linear algebra.

An excellent book. I had to review linear algebra before teaching a course and this was great. Almost as good as Strang. Difference between excellent and more excellent. Money well spent.

The book has a support site with free legal PDF version as well as complementary materials like source codes, links to authors' courses at Stanford and UCLA which are great. So you can check before you buy.The book is rather unusual. I have never seen an introductory level book with mentioning Toeplitz matrices, Kalman filters, FFT, matrix norms and many others. Authors convey the ideas via real-world examples, which in my view is very good. Supplementary materials with the sources codes help to try things out.As of the cons, I would highlight several things. Almost all material is based on QR factorization and its' properties, there is no material on eigenvalues and SVD factorization, which are very important and useful topics in many tasks (just check SVD application on any search engine). Authors have provided source codes on Julia language which is not super popular, although it's not hard, Python would be much more convenient in that sort of problems. Also, I think it's important to highlight that the book is not rigorous, not even on Strang's "Linear algebra" level, I would prefer to have a bit more comprehensive mathematics, but I do not think it's a con, it's more likely a "feature" of this book.As a conclusion, Strang himself wrote a good review of the book. If you have spare 50 bucks and interesting in linear algebra you should buy it :)

I really like this book mainly for the clarity of presentation. Much of the abstract algebraic theory isn't here but that's not what I bought it for. The examples are super practical and span (no pun...) a few different disciplines, so the book has relevance for many in data science as well as other linear algebra applications. If you're looking for a more rigorous, abstract, proof-based coverage of the vast topics of linear and matrix algebra, you'll want to go with something else, but that's a strength of this book, IMO.

This is such a great book on so many level's... it starts from the basics and uses very precise nomenclature and reasoning through out the book. The book provides numerous examples of how vectors and matrices are used to represent complex real world data and system. Great sections on Least Squares Fitting (i.e. optimization-lite).Check out Dr. Boyd's site for PDF copy of the book and see for yourself.I bought the hardback copy because I love books and want to support Cambridge Press and the authors fine work... amd I hope you will also. As quality math books go, 50 USD is very fair.Be sure to check the author's website(s) for: (1) additional resources for this book including a ~200 page guide with Julia code for many of the examples, (2) other free books! (convex optimization is amazing as well).The Julia programming language is a free alternative to MATLAB that is going to be the dominant numerical programming language in the future.

This is an excellent book for Linear Algebra and the three things that i really like about the book are:1. The precise use of nomenclature in definitions and explaining concepts2. The books explain concepts with application especially to Machine Learning and Data Science applications3. The free availability of the PDF and slides for the book onlineI would highly recommend this book for anyone looking to pursue graduate level studies in Data Science.

This book is well organized and self contained. Include many interesting exercises and examples from broad spectrum of applied linear algebra including machine learning field: clustering for example.This book can be good companion or the primary book both for linear algebra course or introduction to machine learning