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Tensor rank: Difference between revisions
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(Created page with "{{see also|Machine learning terms}} ==Definition of Tensor Rank== In the field of machine learning, tensors are multi-dimensional arrays that provide a mathematical framework to represent and manipulate data. The rank of a tensor, also known as its ''order'', refers to the number of dimensions or indices required to describe the tensor. Formally, the tensor rank is defined as the number of axes within a tensor. In other words, the tensor rank determines the complexit...") |
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
The rank of a [[tensor]], also known as its ''order'', refers to the number of dimensions or indices required to describe the tensor. Formally, the tensor rank is defined as the number of axes within a tensor. In other words, the tensor rank determines the complexity of the data structure, providing insights into the nature of the underlying data. | |||
==Rank of Common Tensors in Machine Learning== | ==Rank of Common Tensors in Machine Learning== | ||