This article provides a detailed overview of numpy.einsum, emphasizing its use of Einstein notation for operations on multi-dimensional arrays, particularly in the context of machine learning. It demonstrates matrix multiplication as a primary use case, outlining how the subscript string indicates input and output dimensions. The explanation includes shapes of operands and how dimensions are contracted, allowing readers to understand the mechanism behind einsum. Furthermore, the article hints at its broader applications beyond basic operations, proposing its power in efficiently handling complex tensor calculations.
The einsum function simplifies complex tensor operations while allowing flexibility in specifying output dimensions, making it highly useful for machine learning applications.
Using Einstein notation, einsum’s subscript string explicitly describes input-output relations and enables compact multi-dimensional array operations.
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