Construct · 2 fields · 3 citations
One name. Different field. Different construct. One map.
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A geometric object that takes several vectors and produces a number by a rule that is linear in each of them, generalising scalars, vectors, and matrices: its numerical components depend on the coordinate system chosen, but they must change under a change of coordinates in a fixed, definite way so that the object itself is unchanged. Ricci and Levi-Civita's 1900 Méthodes de calcul différentiel absolu founded the calculus of such objects, which Einstein's 1916 Die Grundlage der allgemeinen Relativitätstheorie used to write the metric and stress-energy tensors of general relativity. The referent is defined by its transformation law, and that law is exactly what tells it apart from a mere grid of numbers stored in a computer or a muscle in the body.[1][2]
A multidimensional array of numbers used as the basic data structure of deep-learning frameworks: it has a shape (a size along each axis), a rank (how many axes it has), and stored values, and the framework moves it efficiently across hardware to compute with it. Paszke and colleagues' 2019 PyTorch: An imperative style, high-performance deep learning library built such an object at the centre of its system, as did the earlier TensorFlow. Mathematicians regard this as a loose use of the word, because it keeps the array but drops the transformation law that defines the mathematical tensor, so despite the shared name it is closer to a plain table of numbers than to a coordinate-invariant geometric object or a muscle.[3]
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