👉 Matrix computing is a computational paradigm that leverages the power of matrix operations to solve complex problems efficiently, particularly in fields like machine learning, data science, and scientific computing. It involves representing data, operations, and transformations as matrices and performing these operations using matrix algebra, which can significantly speed up computations compared to traditional scalar or vector methods. This approach is especially beneficial for large-scale data processing, as it allows for parallel processing and optimized memory usage. By utilizing matrix decomposition techniques, such as Singular Value Decomposition (SVD) or Principal Component Analysis (PCA), matrix computing can extract meaningful insights from data and perform tasks like dimensionality reduction, image compression, and pattern recognition with enhanced performance and scalability.
