Applications of Linear Algebra in Machine Learning
A detailed guide to applications of linear algebra in machine learning. Covers key methods, mathematical significance, and real-world applications.
Mathematics Category
Linear Algebra
22 articles
≈ 220 min total reading
Change of Basis and Similarity Transformations
A detailed guide to change of basis and similarity transformations. Covers key methods, mathematical significance, and real-world applications.
Determinants: Properties, Computation, and Geometric Meaning
A detailed guide to determinants: properties, computation, and geometric meaning. Covers key methods, mathematical significance, and real-world application
Diagonalization: Conditions and Applications
A detailed guide to diagonalization: conditions and applications. Covers key methods, mathematical significance, and real-world applications.
Eigenvalues and Eigenvectors: Computation and Properties
A detailed guide to eigenvalues and eigenvectors: computation and properties. Covers key methods, mathematical significance, and real-world applications.
Inner Product Spaces: Definition and Key Properties
A detailed guide to inner product spaces: definition and key properties. Covers key methods, mathematical significance, and real-world applications.
Least Squares Approximation and Data Fitting
A detailed guide to least squares approximation and data fitting. Covers key methods, mathematical significance, and real-world applications.
Linear Algebra
Learn about linear algebra — covering Linear Algebra, Linear Fundamentals, and the role of linear mathematics in this fundamental mathematical topic.
Linear Algebra in Computer Graphics
A detailed guide to linear algebra in computer graphics. Covers key methods, mathematical significance, and real-world applications.
Linear Independence, Basis, and Dimension
A detailed guide to linear independence, basis, and dimension. Covers key methods, mathematical significance, and real-world applications.
Linear Transformations: Kernel, Image, and Rank-Nullity
A detailed guide to linear transformations: kernel, image, and rank-nullity. Covers key methods, mathematical significance, and real-world applications.
LU Decomposition and Matrix Factorization
A detailed guide to lu decomposition and matrix factorization. Covers key methods, mathematical significance, and real-world applications.
Markov Chains and Stochastic Matrices
A detailed guide to markov chains and stochastic matrices. Covers key methods, mathematical significance, and real-world applications.
Matrices: Operations, Determinants, and Applications
A detailed guide to matrices: operations, determinants, and applications. Covers key methods, mathematical significance, and real-world applications.
Matrix Inverse: Computation and Properties
A detailed guide to matrix inverse: computation and properties. Covers key methods, mathematical significance, and real-world applications.
Orthogonality: Orthogonal Vectors and Projections
A detailed guide to orthogonality: orthogonal vectors and projections. Covers key methods, mathematical significance, and real-world applications.
Singular Value Decomposition: Theory and Applications
A detailed guide to singular value decomposition: theory and applications. Covers key methods, mathematical significance, and real-world applications.
Symmetric Matrices and the Spectral Theorem
A detailed guide to symmetric matrices and the spectral theorem. Covers key methods, mathematical significance, and real-world applications.
Systems of Linear Equations: Gaussian and Gauss-Jordan Elimination
A detailed guide to systems of linear equations: gaussian and gauss-jordan elimination. Covers key methods, mathematical significance, and real-world appli
Vector Cross Product and Geometry in R3
A detailed guide to vector cross product and geometry in r3. Covers key methods, mathematical significance, and real-world applications.
Vector Spaces: Axioms, Subspaces, and Examples
A detailed guide to vector spaces: axioms, subspaces, and examples. Covers key methods, mathematical significance, and real-world applications.
Vectors: Addition, Scalar Multiplication, and Dot Products
A detailed guide to vectors: addition, scalar multiplication, and dot products. Covers key methods, mathematical significance, and real-world applications.