Linear algebra.

Includes bibliographical references and index.

Matrices and special types of square matrices -- System of linear equations and determinants -- Vector spaces and isomorphism -- Eigenvalues and eigenvectors -- Inner product spaces and quadratic forms -- Linear programming.

Now, much more than arithmetic and geometry, mathematics today is a diverse discipline that deals with data, measurements and observations from science, with inference, deduction, and proof; and with mathematical models of natural phenomena, of human behavior, and of social systems. Linear Algebra is a continuous form of mathematics and is applied throughout science and engineering because it allowsyou to model natural phenomena and to compute them efficiently. Because it is a form of continuous and not discrete mathematics, a lot of computer scientists don't have a lot of experience with it. Linear algebra is vital in multiple areas of science in general. Because linear equations are so easy to solve, practically every areaof modern scienve contains models where equations are approximated by linearequations and solving for the system helps the theory develop. The concepts of Linear Algebra are crucial for understanding the theory behing Machine Learning, especially for Deep Learning. They give you better intuition for how algorithms really work under the hood, which enables you to make better decisions. This book will give you an introduction to the most important concepts of Linear Algebra. This book contains six chapters. The students will become competent in solving linear equations, performing matrix algebra, calculating determinants and finding eigenvalues and eigenvectors.