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A sparse matrix stores only its nonzero entries, saving huge amounts of memory when most entries are zero.
In-context learning (ICL) means a model learns from examples provided in the input itself, without updating its parameters.
Matrix calculus extends single-variable derivatives to matrices so we can differentiate functions built from matrix multiplications, traces, and norms.
Matrix operations like multiplication and transpose combine or reorient data tables and linear transformations in predictable ways.
Linear algebra studies vectors, linear combinations, and transformations that preserve addition and scalar multiplication.