New term entry on the built in function for Numpy named 'svd'

content/numpy/concepts/built-in-functions/terms/svd/svd.md

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+---
+Title: '.svd()'
+Description: 'The `.svd()` built-in function performs the Singular Value Decomposition (SVD) on a matrix, breaking it down into singular vectors and values.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+  - 'Machine Learning'
+Tags:
+  - 'Linear Algebra'
+  - 'Machine Learning'
+  - 'NumPy'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+  - 'paths/data-science'
+  - 'paths/data-science-foundaions'
+---
+
+The **`.svd()`** function is a mathematical technique that decomposes a matrix into three simpler matrices: it factorizes the matrix `a` into two unitary matrices `U` and `Vh`, along with a 1-D array `s` of singular values (real and non-negative). This decomposition satisfies the equation `a = U @ S @ Vh`, where `S` is a suitably shaped matrix of zeros with `s` as its main diagonal.
+## Syntax
+
+```pseudo
+numpy.linalg.svd(a, full_matrices=True, compute_uv=True, hermitian=False)
+```
+`.svd()` provides the following arguments:
+
+- `a`: This parameter represents the input matrix to be decomposed, where `a.ndim>=2`. It is the matrix on which Singular Value Decomposition will be performed.
+- `full_matrices`: This parameter determines whether the function computes full-sized or reduced-sized matrices `U` and `Vh`. If `full_matrices` is set to `True`, the function computes the full-sized matrices. If set to `False`, it computes only the essential parts of `U` and `Vh`. The default value is `True`.
+- `compute_uv`: This parameter specifies whether the function computes the left-singular vectors (`U`) and right-singular vectors (transpose of `V`) in addition to the singular values. When `compute_uv` is set to `True`, the function computes `U` and `Vh`. If set to `False`, it only computes the singular values. The default value is `True`.
+- `hermitian`: This parameter indicates whether the input matrix `a` is Hermitian, meaning it is equal to its conjugate transpose. When `hermitian` is set to `True`, the function assumes that a is Hermitian and uses a more efficient algorithm tailored for such matrices. If set to `False`, it uses a general algorithm. The default value is `False`.
+
+## Example
+The following example demonstrates various scenarios of Singular Value Decomposition (SVD) and uses `.svd()` using `NumPy`:
+
+```py
+# The following example demonstrates a scenario of Singular Value Decomposition (SVD) for a 2D matrix using NumPy:
+import numpy as np
+
+# Create a 2D array
+A = np.random.randn(5, 3)  # Creating a random 2D matrix
+
+print("Original 2D matrix:") 
+print(A) 
+
+# Compute the factor by Singular Value Decomposition
+U, S, V = np.linalg.svd(A)  
+
+# Print the result 
+print("\nFactor of the given array by Singular Value Decomposition:") 
+print("\nU=", U, "\n\ns=", S, "\n\nV=", V)
+```