Expanding the shape of an operand in a matrix math operation to dimensions compatible for that operation. For instance, linear algebra requires that the two operands in a matrix addition operation must have the same dimensions. Consequently, you can’t add a matrix of shape (m, n) to a vector of length n. Broadcasting enables this operation by virtually expanding the vector of length n to a matrix of shape (m,n) by replicating the same values down each column. For example, given the following definitions, linear algebra prohibits A+B because A and B have different dimensions:
However, broadcasting enables the operation A+B by virtually expanding B to:
Thus, A+B is now a valid operation:
See the following description of broadcasting in NumPy for more details. The post What is broadcasting in Machine Learning? appeared first on Data Science PR. Originally from Machine Learning & AI – Data Science PR https://ift.tt/3rtLQsP
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