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Introduction to Linear Algebra, Fourth Edition, by Gilbert Strang
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Gilbert Strang's textbooks have changed the entire approach to learning linear algebra -- away from abstract vector spaces to specific examples of the four fundamental subspaces: the column space and nullspace of A and A'.
Introduction to Linear Algebra, Fourth Edition includes challenge problems to complement the review problems that have been highly praised in previous editions. The basic course is followed by seven applications: differential equations, engineering, graph theory, statistics, fourier methods and the FFT, linear programming, and computer graphics.
Thousands of teachers in colleges and universities and now high schools are using this book, which truly explains this crucial subject.
Chapter 1: Introduction to Vectors; Chapter 2: Solving Linear Equations; Chapter 3: Vector Spaces and Subspaces; Chapter 4: Orthogonality; Chapter 5: Determinants; Chapter 6: Eigenvalues and Eigenvectors; Chapter 7: Linear Transformations; Chapter 8: Applications; Chapter 9: Numerical Linear Algebra; Chapter 10: Complex Vectors and Matrices; Solutions to Selected Exercises; Final Exam. Matrix Factorizations. Conceptual Questions for Review. Glossary: A Dictionary for Linear Algebra Index Teaching Codes Linear Algebra in a Nutshell.
- Sales Rank: #110706 in Books
- Brand: Brand: Wellesley Cambridge Press
- Published on: 2009-02-10
- Ingredients: Example Ingredients
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.18" w x 7.52" l, 2.60 pounds
- Binding: Hardcover
- 584 pages
Features
- Used Book in Good Condition
About the Author
Gilbert Strang is a Professor of Mathematics at Massachusetts Institute of Technology and an Honorary Fellow of Balliol College, of the University of Oxford, UK. His current research interests include linear algebra, wavelets and filter banks, applied mathematics, and engineering mathematics. He is the author or co-author of six textbooks and has published a monograph with George Fix titled “An Analysis of the Finite Element Method.” Professor Strang served as SIAM’s president from 1999-2000, chaired the U.S. National Committee on Mathematics from 2003–2004, and won the Neumann Medal of the US Association of Computational Mechanics in 2005. He is a fellow of the American Academy of Arts and Sciences.
Most helpful customer reviews
37 of 38 people found the following review helpful.
Indispensable resource for any data scientist
By Dan D. Gutierrez
My bookshelves are lined with materials that support my work in data science and machine learning. I have a large section of mathematics books including several on the subject of linear algebra. For many years my “go to” text on linear algebra was an old 2nd edition of MIT Professor Gilbert Strang’s seminal book on the subject that I picked up at a swap meet. To my surprise, the good professor recently sent me a copy of his latest and greatest 5th edition of “Introduction to Linear Algebra” (Wellesley-Cambridge Press).
I found the new edition to be even better than previous editions. For one it is now 574 pages versus my old copy’s 374. I also found the book to be impressively re-tooled for educational purposes. The chapters contain useful “Review of the Key Ideas” sections, worked examples, and well thought out problem sets (with special “Challenge Problems” for those who want to dive deeper). Gilbert Strang’s textbooks have changed the entire approach to learning linear algebra – away from abstract vector spaces to specific examples of the four fundamental subspaces: the column space and nullspace of A and A’.
The chapters directly apply to the needs of data scientists wishing to establish a firm foundation for how machine learning happens behind the scenes. All chapters are superbly crafted, but my favorites are: Chapter 7 because SVD plays an important role in Principal Component Analysis for dimensionality reduction as well as PCA regression; Chapter 10 as it enhances the math subject matter with practical applications; Chapter 11 which is a nice adjunct to the pure math content and reminds me of portions of the old “Numerical Methods” (Prentice-Hall) text by G. Dahlquist et al that I used in my early days of data science; and Chapter 12 which is perfect for data scientists who want to see the relationship with statistics and probability.
Strang’s new edition is a great launching point for newbies as well as practicing data scientists to gain a foothold in the theory behind the technology. If you feel a bit insecure with your mathematical prowess when reading the statistical leaning bible “Elements of Statistical Learning” by Hastie, Tibshirani and Friedman (a group of high-profile Stanford researchers), then Strang’s book is the best way to lay a firm foundation.
Gilbert Strang is a Professor of Mathematics at Massachusetts Institute of Technology and an Honorary Fellow of Balliol College, of the University of Oxford. His current research interests include linear algebra, wavelets and filter banks, applied mathematics, and engineering mathematics. He is the author or co-author of eight textbooks. He is a Fellow of the American Academy of Arts and Sciences and a member of the National Academy of Sciences.
The book also comes with an excellent web resource which includes downloadable sections (PDFs) of many chapters, a complete chapter-by-chapter solutions manual for the problem sets, and practice exam questions. The book is used as the textbook for MIT’s undergrad linear algebra course 18.06. It is also the book used in MIT’s Open Courseware class on the subject, complete with video lectures. This means you can take a full-fledged MIT course to help you become well-versed with this important subject matter. I highly recommend this book for any up-and-coming data scientist.
I do have a big complaint with this new book! It’s going to sap a lot of time from my busy schedule because with such a great learning resource in my hands, I know myself, I’m going to spend time “re-learning” the subject for the nth time, doing the problem sets, and thinking hard about how important math is to a firm understanding of machine learning. I don’t have time for this!
11 of 12 people found the following review helpful.
Deserves the money spent
By H. RAGHEB
I never thought that linear algebra is that useful in real life. I was enrolled in a master program and my first course was computer vision. There were massive amount of math and mostly were linear algebra. I felt like I lacked all enough comfort with linear algebra and thought that I could have understood many topics much better if I got a good book. Hell, this book is awesome. It has all what I need specially SVD and PCA. I don't care much about solving problems as to understand the meaning of linear algebra and its logic. I must admit that prior reading this book I had good grasp of what a matrix is and how we can formulate our data as a matrix, which opened the world of applications I can apply all the techniques of linear algebra on. Dr.Gilbert is a man who knows how to deliver high quality information to you, but you as a reader must also have the role of analyzing what you read and try to find an application of interest in your head where you can practice what you read. I looked at 10s of books before deciding to buy this one, and the only book which I felt comfortable with was this one. The only problem is that this book is expensive, but in my opinion, this books deserves every dollar and is a must in any machine lerning, artificial intelligence, computer vision, game programer or any field engineer library
8 of 9 people found the following review helpful.
Amazing book. I watched Professor Strang's Linear Algebra lectures ...
By shortlaker
Amazing book. I watched Professor Strang's Linear Algebra lectures on Youtube before buying this book. I delayed in buying this book because I thought that all the information on linear algebra can be found on the internet for free. Why do I have to buy a $90 book? But I had a hard time understanding linear algebra even after taking a course in college, watching youtube videos, and reading explanations online. So, I gave in and bought this book. Now, I realize what an amazing investment this has been. Worth every penny. In fact, it might be too cheap that this book is only worth $90. I wish I had bought this book many years ago. Professor Strang is a genius in his understanding of math. But more important is that he is a genius in explaining difficult concepts and making it easy to understand. If you're trying to learn and/or understand linear algebra, save yourself a lot of trouble by buying this book.
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