The teaching and learning of linear algebra have evolved significantly over recent decades, underpinned by diverse approaches ranging from theoretical expositions to dynamic, model-based environments.
Nearly all big science, machine learning, neural network, and machine vision applications employ algorithms that involve large matrix-matrix multiplication. But multiplying large matrices pushes the ...
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