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Linear Algebra Operations | Getting into NumPy Basics
Getting into NumPy Basics
course content

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

  1. Compute the dot product of the arrays.
  2. Transpose the first array.
  3. Compute the inverse of the second array.

Task

  1. Compute the dot product of the arrays.
  2. Transpose the first array.
  3. Compute the inverse of the second array.

Mark tasks as Completed
Switch to desktop for real-world practiceContinue from where you are using one of the options below

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

  1. Compute the dot product of the arrays.
  2. Transpose the first array.
  3. Compute the inverse of the second array.

Mark tasks as Completed
Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 5
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