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Getting into NumPy Basics
course content

Course Content

Getting into NumPy Basics

Getting into NumPy Basics

IntroductionCreating NumPy ArraysManipulating and IndexingBasic Statistical OperationsLinear Algebra OperationsReshaping and StackingSaving and Loading Arrays to/from Files

bookLinear 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.

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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.

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