machine-learning-math

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SENATOROVAI / QR-decomposition-solver-course

QR decomposition, or QR factorization, is a fundamental linear algebra method that decomposes a matrix into a product of an orthogonal matrix and an upper triangular matrix. It is widely used for solving linear least squares problems, computing eigenvalues, Gram-Schmidt, Householder reflections, or Givens rotations.Solver

python data-science linear-algebra solver matrix-factorization least-squares scientific-computing qr-decomposition numerical-linear-algebra gram-schmidt givens-rotations qr-factorization senatorov householder-reflections machine-learning-math qr-solver solver-qr

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Python

👁 cholesky-decomposition-solver-course

SENATOROVAI / cholesky-decomposition-solver-course

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Cholesky decomposition is a matrix factorization method that decomposes a symmetric, positive-definite matrix into the product of a lower triangular matrix and its transpose (i.e., ). LU decomposition for solving linear equations and is widely used in Monte Carlo simulations, Kalman filters, and econometrics. Solver

python data-science optimization linear-algebra solver matrix-factorization scientific-computing numerical-methods cholesky-decomposition cholesky-factorization senatorov machine-learning-math python-cholesky-implementation linalg-cholesky-from-scratch cholesky-solver solver-cholesky

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Python

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