Non-zero Elements
In data science, non-zero elements refer to the values in a matrix or vector that are not equal to zero. These elements are important in various mathematical operations such as matrix multiplication, eigenvalue calculation, and singular value decomposition. Non-zero elements are also used in sparse matrix representation, where only the non-zero elements are stored to save memory and computation time. The number of non-zero elements in a matrix or vector is called the sparsity of the matrix or vector. Sparsity is an important concept in machine learning, where it is used to optimize algorithms for large datasets with many zero values.
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