Course Content
Getting into NumPy Basics
Linear Algebra Operations
NumPy
offers a plethora of functions for executing linear algebra operations on arrays, including matrix multiplication, transposition, inversion, and decomposition. Key functions include:
dot()
: Computes the dot product of two arrays;transpose()
: Transposes an array;inv()
: Computes the inverse of a matrix;linalg.svd()
: Performs the singular value decomposition of a matrix;linalg.eig()
: Determines the eigenvalues and eigenvectors of a matrix.
Task
- Compute the dot product of the arrays.
- Transpose the first array.
- Compute the inverse of the second array.
Task
- Compute the dot product of the arrays.
- Transpose the first array.
- Compute the inverse of the second array.
Everything was clear?
NumPy
offers a plethora of functions for executing linear algebra operations on arrays, including matrix multiplication, transposition, inversion, and decomposition. Key functions include:
dot()
: Computes the dot product of two arrays;transpose()
: Transposes an array;inv()
: Computes the inverse of a matrix;linalg.svd()
: Performs the singular value decomposition of a matrix;linalg.eig()
: Determines the eigenvalues and eigenvectors of a matrix.
Task
- Compute the dot product of the arrays.
- Transpose the first array.
- Compute the inverse of the second array.