👉 Matrix multiplication, often denoted as \( A \times B \) (where \( A \) and \( B \) are matrices), is a fundamental operation in linear algebra. Given two matrices \( A \) (of dimensions \( m \times n \)) and \( B \) (of dimensions \( n \times p \)), the product \( C = A \times B \) results in a matrix \( C \) of dimensions \( m \times p \). Each element \( C_{ij} \) in the resulting matrix is calculated as the dot product of the \( i \)-th row of \( A \) and the \( j \)-th column of \( B \). This operation is crucial for various applications, including solving systems of linear equations, transforming coordinate systems, and in deep learning for neural network operations.
