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.
