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
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discover the key concepts covered in this coursedefine what's meant by a vector, a vector notation, a matrix notation, and an ordered set notationoutline how to enumerate properties of matrices and vectorsrecognize several types of matrix operationsmathematically define matrix decompositionmathematically define QR and Cholesky decomposition
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install libraries in Pythonperform LU decompositionperform QR decompositionperform Cholesky decompositionsummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview2m 20sUP NEXT
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2.Vectors and Notation6m 30s
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3.Linear Transformations with Matrices10m 4s
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4.Matrix Types13m 22s
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5.Matrix Decomposition7m 30s
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6.QR and Cholesky Decomposition9m 25s
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7.Getting Set Up in Python4m 6s
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8.Performing LU Decomposition in Python7m 45s
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9.Performing QR Decomposition in Python7m 13s
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10.Performing Cholesky Decomposition in Python5m 1s
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11.Course Summary1m 51s
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