Supraja S
RV PU College, Jayanagar
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http://doi.org/10.37648/ijrst.v15i04.006
Modern machine learning is, at its core, large-scale numerical linear algebra (NLA): multiplying matrices, solving least-squares systems, computing eigenvectors/SVDs, and approximating kernels efficiently. As datasets and model sizes grow, classical “exact” methods often become too slow or too memory-heavy, making randomized and iterative NLA essential. This paper surveys where NLA appears in machine learning pipelines, which algorithms matter most (direct, iterative, and randomized), and how Python practitioners implement them using NumPy/SciPy and GPU-enabled frameworks. We also include a comparative analysis that connects algorithmic choices to accuracy, runtime, and scalability.
Keywords: machine learning; NLA; SVDs; NumPy/SciPy and GPU-enabled
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