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.

Gilbert Strang has been teaching Linear Algebra at Massachusetts Institute of Technology (MIT) for over fifty years. His online lectures for MIT's OpenCourseWare have been viewed over ten million times. He is a former President of the Society for Industrial and Applied Mathematics and Chair of the Joint Policy Board for Mathematics. Professor Strang is the author of twelve books, including the bestselling classic Introduction to Linear Algebra (2016), now in its fifth edition.

Preface; 1. Vectors and Matrices; 2. Solving Linear Equations Ax = b; 3. The Four Fundamental Subspaces; 4. Orthogonality; 5. Determinants and Linear Transformations; 6. Eigenvalues and Eigenvectors; 7. The Singular Value Decomposition (SVD); 8. Learning from Data; Appendix 1. The Ranks of AB and A + B; Appendix 2. Eigenvalues and Singular Values: Rank One; Appendix 3. Counting Parameters in the Basic Factorizations; Appendix 4. Codes and Algorithms for Numerical Linear Algebra; Appendix 5. Matrix Factorizations; Appendix 6. The Column-Row Factorization of a Matrix; Appendix 7. The Jordan Form of a Square Matrix; Appendix 8. Tensors; Appendix 9. The Condition Number; Appendix 10. Markov Matrices and Perron-Frobenius; Index; Index of Symbols; Six Great Theorems / Linear Algebra in a Nutshell.