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 stephenmalina.com
Matrix Potpourri - Stephen Malina
| | hadrienj.github.io
Introduction to diagonal and symmetric matrices, unit and orthogonal vectors using Python/Numpy examples and drawings
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| | In this post, we will see special kinds of matrix and vectors the diagonal and symmetric matrices, the unit vector and the concept of orthogonality.
| | mattbaker.blog
Linear algebra over rings | Matt Baker's Math Blog
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| | Test your intuition: is the following true or false? Assertion 1: If $latex A$ is a square matrix over a commutative ring $latex R$, the rows of $latex A$ are linearly independent over $latex R$ if and only if the columns of $latex A$ are linearly independent over $latex R$. (All rings in this post...
| | leanprover-community.github.io
Maths in Lean: linear algebra
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| | [AI summary] A technical documentation entry for the Lean mathematical library detailing definitions and operations for semimodules, modules, matrices, linear maps, and bilinear forms
| | www.jeremykun.com
Singular Value Decomposition Part 1: Perspectives on Linear Algebra | Math Programming
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| The singular value decomposition (SVD) of a matrix is a fundamental tool in computer science, data analysis, and statistics. It's used for all kinds of applications from regression to prediction, to finding approximate solutions to optimization problems. In this series of two posts we'll motivate, define, compute, and use the singular value decomposition to analyze some data. (Jump to the second post) I want to spend the first post entirely on motivation and background.