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

Ebook Free Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares

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Review

'Introduction to Applied Linear Algebra fills a very important role that has been sorely missed so far in the plethora of other textbooks on the topic, which are filled with discussions of nullspaces, rank, complex eigenvalues and other concepts, and by way of 'examples', typically show toy problems. In contrast, this unique book focuses on two concepts only, linear independence and QR factorization, and instead insists on the crucial activity of modeling, showing via many well-thought out practical examples how a deceptively simple method such as least-squares is really empowering. A must-read introduction for any student in data science, and beyond!' Laurent El Ghaoui, University of California, Berkeley'This book explains the least squares method and the linear algebra it depends on - and the authors do it right!' Gilbert Strang, Massachusetts Institute of Technology'The kings of convex optimization have crossed the quad and produced a wonderful fresh look at linear models for data science. While for statisticians the notation is a bit quirky at times, the treatise is fresh with great examples from many fields, new ideas such as random featurization, and variations on classical approaches in statistics. With tons of exercises, this book is bound to be popular in the classroom.' Trevor Hastie, Stanford University, California'Boyd and Vandenberghe present complex ideas with a beautiful simplicity, but beware! These are very powerful techniques! And so easy to use that your students and colleagues may abandon older methods. Caveat lector!' Robert Proctor, Stanford University, California

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Book Description

A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance.

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Product details

Hardcover: 474 pages

Publisher: Cambridge University Press; 1 edition (August 23, 2018)

Language: English

ISBN-10: 1316518965

ISBN-13: 978-1316518960

Product Dimensions:

7.7 x 1 x 10 inches

Shipping Weight: 2.7 pounds (View shipping rates and policies)

Average Customer Review:

4.7 out of 5 stars

5 customer reviews

Amazon Best Sellers Rank:

#32,192 in Books (See Top 100 in Books)

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.

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 :)

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

The best book I have ever found for anyone who wants to work on machine learning/deep neural network etc. Lots of practical knowledge points and examples, in the mean time without too much difficult pure math games. Strongly recommend.

The best part of this book is that it requires no prior experience, and by the end you’re able to apply what you’ve learned to sophisticated machine learning and control problems.

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Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares PDF

Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares PDF

Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares PDF

Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares PDF