Homogeneity: T(c * u) = c * T(u) for any scalar c and vector u. Additivity: T(u + v) = T(u) + T(v) for any vectors u and v. The range of a linear transformation T is the set of all vectors that can be ...

Abstract: This book contains a detailed discussion of the matrix operation, its properties, and its applications in finding the solution of linear equations and determinants. Linear algebra is a ...

Abstract: This book contains a detailed discussion of the matrix operation, its properties, and its applications in finding the solution of linear equations and determinants. Linear algebra is a ...

Vector spaces, linear transformation, matrix representation, inner product spaces, isometries, least squares, generalised inverse, eigen theory, quadratic forms, norms, numerical methods. The fourth ...

This is a subject I struggled with the first time I took it. Ironically, this was the engineering version of it. It wasn't until I took the rigorous, axiomatic version that everything clicked.

This course aims to develop your knowledge in the mathematics topics of linear algebra and calculus, which provides the basic mathematics foundation that is necessary for anyone pursuing a computing ...

Linear algebra is essential for understanding core data science concepts like machine learning, neural networks, and data transformations. Different books cater to various needs. Some focus on ...