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The SingularValueDecomposition(x,opts) or SVD(x,opts) commands compute a singular value decomposition of one or more matrices in x

$\mathrm{with}\left(\mathrm{DeepLearning}\right)\:$

$t≔\mathrm{Constant}\left(\mathrm{LinearAlgebra}:-\mathrm{VandermondeMatrix}\left(⟨3.\,5.\,7.⟩\right)\right)$

$t≔\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [3,\; 3]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]$

$\mathrm{SingularValueDecomposition}\left(t\right)$

$\left[\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [3]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]\,\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [3,\; 3]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]\,\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Shape:\; [3,\; 3]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]\right]$

The DeepLearning,Tensor,SingularValueDecomposition command was introduced in Maple 2018.

For more information on Maple 2018 changes, see Updates in Maple 2018.