Difference between revisions of "What is Tensor?"
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− | In simplifying terms, we can think of tensors as multidimensional arrays of numbers, as a generalization of scalars, vectors, and matrices. | + | In simplifying terms, we can think of tensors as multidimensional arrays of numbers, as a generalization of scalars, vectors, and matrices. <ref>Introduction to S. Raschka. 2017. Artificial Neural Networks and Deep Learning, A Practical Guide with Applications in Python. p.7. http://leanpub.com/ann-and-deeplearning</ref> |
# Rank 0 Tensor, Scalar, {{#tag:math|\mathbb{R} }} | # Rank 0 Tensor, Scalar, {{#tag:math|\mathbb{R} }} |
In simplifying terms, we can think of tensors as multidimensional arrays of numbers, as a generalization of scalars, vectors, and matrices. [1]
When we describe tensors, we refer to its “dimensions” as the rank (or order) of a tensor, which is not to be confused with the dimensions of a matrix. For instance, an m × n matrix, where m is the number of rows and n is the number of columns, would be a special case of a rank-2 tensor. A visual explanation of tensors and their ranks is given is the figure below.
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