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.
Section 1. Chapter 5
