Linear Algebra
concepts · 37 notes linked
Related: Singular Value Decomposition · Gilbert Strang · Mit · Wikipedia · Matrix Operations · Stata · Mathematics · Matrix Decomposition
Notes
- #009 The Singular Value Decomposition(SVD) - illustrated in Python - Master Data Science — Step-by-step SVD tutorial with Python implementation and image reconstruction examples
- An Intuitive Guide to Linear Algebra — Spreadsheet-metaphor intuition for matrices, transformations, and eigenvectors
- Basic Linear Algebra for Deep Learning and Machine Learning Python Tutorial — Introductory linear algebra tutorial for ML and deep learning with Python
- Conjugate Transpose -- from Wolfram MathWorld — Definition and notation of the matrix conjugate transpose operation
- Conjugate transpose - Wikipedia — Conjugate transpose operation, notations, and matrix classification by self-adjointness
- Covariance matrix - Wikipedia — Square matrix encoding pairwise covariances of a random vector
- Cramer's rule - Wikipedia — Explicit determinant-based formula solving systems of linear equations
- Data Science from Scratch, 2nd Edition — Linear algebra foundations for data science implemented in Python from scratch
- Episode 62 - Tai-Danae Bradley — Kevin Knudson — Tai-Danae Bradley explains SVD and its parallel to category theory adjunction
- Fourier transform for dummies — Intuitive explanations of Fourier transform from physics and engineering perspectives
- Free Mathematics Books — Large curated directory of freely available mathematics textbooks and lecture notes
- Frobenius Norm -- from Wolfram MathWorld — Definition of Frobenius norm as square root of sum of squared element magnitudes
- General linear group - Wikipedia — Group of invertible n-by-n matrices under multiplication
- Gram matrix - Wikipedia — Matrix of pairwise inner products encoding geometric structure of a vector set
- He made linear algebra fun — Gilbert Strang retires after 66 years making linear algebra accessible globally
- Inner Product — Inner product concept connecting linear algebra and digital signal processing
- Interactive Linear Algebra — Free interactive linear algebra textbook from Georgia Tech
- Intuitively, what is the difference between Eigendecomposition and Singular Value Decomposition? — Mathematical relationship showing SVD generalizes eigendecomposition to rectangular matrices
- Invitation to Another Dimension — Interactive visual progression from linear functions to matrices and vector fields
- Kernel (linear algebra) - Wikipedia — Subspace of a linear map's domain mapped to the zero vector
- Linear Algebra Archives - The Stata Blog — Stata blog archive of intuitive matrix visualization posts
- Linear Algebra Done Right — Sheldon Axler's linear algebra textbook video companion page
- Linear Algebra Toolkit — Interactive web modules for practicing core linear algebra procedures
- Linear algebra tutorial in four pages — Compact four-page linear algebra reference covering vectors through eigenvalues
- MIT 18.06 Linear Algebra, Spring 2005 : MIT OpenCourseWare : Free Download, Borrow, and Streaming : Internet Archive — MIT OpenCourseWare video lectures on linear algebra by Gilbert Strang
- Making sense of principal component analysis, eigenvectors & eigenvalues — Geometric and computational walkthrough of PCA via eigendecomposition and SVD
- Matrix Trace -- from Wolfram MathWorld — Definition and properties of the matrix trace operation
- Matrix multiplication - Wikipedia — Definition, properties, and complexity of matrix multiplication
- Matrix norm - Wikipedia — Taxonomy of matrix norms: operator, entry-wise, and Schatten types
- Singular Value Decomposition Part 1: Perspectives on Linear Algebra — Intuitive motivation for SVD as data approximation via matrix factorization
- Symmetric matrix - Wikipedia — Square matrix equal to its transpose with real eigenvalues
- The 'Useless' Perspective That Transformed Mathematics — Representation theory maps abstract groups to concrete matrices
- Understanding matrices intuitively, part 1 - The Stata Blog — Visual grid-transformation method for intuiting matrix operations
- Understanding matrices intuitively, part 2, eigenvalues and eigenvectors - The Stata Blog — Geometric intuition for eigenvalues and eigenvectors via matrix transforms
- Vector Norms: A Quick Guide | Built In — L1, L2, Lp, and Linfinity vector norms explained for ML
- Watch: MIT students give longtime professor a standing ovation after his last lecture — Gilbert Strang retires from MIT after 63 years teaching linear algebra
- tensor-decomposition — Introduction to tensors and their decomposition using Young diagrams