Matrix Decomposition: Getting Started with Matrix Decomposition

Math    |    Intermediate
  • 11 videos | 1h 15m 7s
  • Includes Assessment
  • Earns a Badge
Matrix decomposition refers to the process of expressing a matrix as the product of other matrices. These factorized matrices are a lot easier to work with than the original matrix, as they usually possess specific properties desirable in the contexts of various mathematical procedures. Use this course to learn how to use matrix decomposition. Explore precisely what matrices and vectors are and how they're used. Then, study various matrix operations, such as computing the transpose and the inverse of a matrix. Moving on, identify why matrices are great for expressing linear transformations of points in a coordinate space. Work with important transformations, such as shearing, reflection, and rotation. Implement the LU, QR, and Cholesky decompositions and examine their applicability and restrictions. Upon completion, you'll know when and how to implement various matrix decompositions.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    define what's meant by a vector, a vector notation, a matrix notation, and an ordered set notation
    outline how to enumerate properties of matrices and vectors
    recognize several types of matrix operations
    mathematically define matrix decomposition
    mathematically define QR and Cholesky decomposition
  • install libraries in Python
    perform LU decomposition
    perform QR decomposition
    perform Cholesky decomposition
    summarize the key concepts covered in this course

IN THIS COURSE

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