This course is part of the Mathematics for Machine Learning and Data Science Specialization by DeepLearning.AI. After completing this course, learners will be able to: Represent data as vectors and ...

<meta property="og:title" content="6-1 Matrix and matrix multiplication "/> <meta property="og:url" content="https://davidnie-xiaonan.github.io/Linear_Algebra_note ...

This is the revised and expanded edition of the problem book Linear Algebra: Challenging Problems for Students, now entitled Problems in Linear Algebra and Matrix Theory. This new edition contains ...

This book serves as an introduction to linear algebra for undergraduate students in data science, statistics, computer science, economics, and engineering. The book presents all the essentials in ...

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 ...

NumPy includes some tools for working with linear algebra in the numpy.linalg module. However, unless you really don’t want to add SciPy as a dependency to your project, it’s typically better to use ...

ABSTRACT: In this paper, a series of bicomplex representation methods of quaternion division algebra is introduced. We present a new multiplication concept of quaternion matrices, a new determinant ...

Abstract: Sparse linear algebra kernels achieve sub-optimal performance due to their poor cache locality. Matrix reordering is an effective pre-processing optimization that improves cache locality and ...