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 ...

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 ...

This code is for Matrix and Vector calculation. This code has "Ratio" type. It has sign bit, unsigned long long denominator and numerator. You can use this Ratio type to calculate exact value (bounded ...

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 ...

Fuzzy normed linear spaces extend conventional normed spaces by integrating a degree of imprecision through fuzzy set theory, thereby quantifying uncertainty in the measurement of vector magnitude. In ...

Elementary set theory and solution sets of systems of linear equations. An introduction to proofs and the axiomatic methods through a study of the vector space axioms. Linear analytic geometry. Linear ...

N. E. Gretsky and J. J. Uhl, Jr. Representations of bounded linear operators on Banach function spaces of vector-valued functions to Banach spaces are given in terms of operator-valued measures. Then ...

Abstract: The multiplication of a sparse matrix with a dense vector is a vital operation in linear algebra, with applications in numerous contexts. After earlier research on FPGA acceleration of this ...