mathematics 28
- 8.3 Applications of SVD
- 8.2 The Geometric Meaning of SVD
- 8.1 Singular Value Decomposition (SVD): Definition
- 7.3 Least Squares
- 7.2 The Gram-Schmidt Process
- 7.1 Orthogonal Vectors and Orthogonal Matrices
- 6.4 The Geometric Meaning of Eigenvalues
- 6.3 Diagonalization
- 6.2 The Characteristic Equation
- 6.1 What Are Eigenvalues and Eigenvectors?
- 5.4 Null Space and Column Space
- 5.3 Dimension
- 5.2 Linear Independence and Basis
- 5.1 Subspaces and Span
- 4.4 Composition of Transformations
- 4.3 3D Linear Transformations
- 4.2 2D Linear Transformations
- 4.1 What is a Linear Transformation?
- 3.3 The Inverse Matrix and the Determinant
- 3.2 Matrix Operations
- 3.1 What is a Matrix?
- 2.3 The Cross Product
- 2.2 Vector Projection
- 2.1 The Dot Product
- 1.4 Magnitude and Unit Vectors
- 1.3 Scalar Multiplication
- 1.2 Vector Addition and Subtraction
- 1.1 What Is a Vector?