linear algebra 28
- SVD Applications - Image Compression, Recommender Systems, and PCA
- SVD Geometric Interpretation - Visualizing Singular Value Decomposition
- Singular Value Decomposition (SVD) - Definition and Computation
- Least Squares Method - Formula and Step-by-Step Solution
- Gram-Schmidt Process - Orthogonalization Step by Step
- Orthogonal Vectors and Orthogonal Matrices - Properties and Examples
- Geometric Meaning of Eigenvalues and Eigenvectors Visualized
- Matrix Diagonalization - How to Diagonalize a Matrix
- Characteristic Equation - How to Find Eigenvalues Step by Step
- Eigenvalues and Eigenvectors - Definition and How to Find Them
- Null Space and Column Space - How to Find Them
- Dimension of a Vector Space - Basis and Dimension Explained
- Linear Independence and Basis - How to Determine If Vectors Are Independent
- Subspace and Span in Linear Algebra - Definition and Examples
- Composition of Linear Transformations and Matrix Multiplication
- 3D Linear Transformations - Rotation Matrices and Scaling
- 2D Linear Transformations - Rotation, Reflection, and Shear
- What Is a Linear Transformation? Definition and Examples
- Inverse Matrix and Determinant - How to Find Them
- Matrix Operations - Matrix Addition and Multiplication Explained
- What Is a Matrix? Introduction to Matrices in Linear Algebra
- Cross Product Formula and Geometric Interpretation
- Vector Projection Formula - How to Project One Vector onto Another
- Dot Product Formula and Geometric Meaning Explained
- Vector Magnitude and Unit Vector - How to Find Them
- Scalar Multiplication of Vectors - Definition and Properties
- Vector Addition and Subtraction - How to Add Vectors
- What Is a Vector? Definition and Examples in Linear Algebra