SENATOROVAI/singular-value-decomposition-svd-solver-course

Singular Value Decomposition (SVD) is a fundamental linear algebra technique that factorizes any into the product of three matrices: are orthogonal matrices containing left and right singular vectors, while sigma is a diagonal matrix of non-negative singular values. It is essential for data reduction, noise removal, and matrix approximation.Solver

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This project helps developers understand and implement Singular Value Decomposition (SVD), a core linear algebra technique. It provides the mathematical theory and practical Python code to factorize a data matrix into three component matrices, which can then be used for tasks like data reduction and noise removal. It is designed for software developers and data scientists who need to build or deeply understand algorithms for data analysis and machine learning.

Use this if you are a developer looking for a comprehensive guide to implementing SVD from scratch in Python, including its mathematical foundations and various applications.

Not ideal if you are an end-user simply looking to apply SVD to your data without needing to understand or implement the underlying algorithms yourself.

Machine Learning Engineering Data Science Algorithms Linear Algebra Implementation Numerical Methods Algorithm Development
No Package No Dependents
Maintenance 10 / 25
Adoption 6 / 25
Maturity 15 / 25
Community 18 / 25

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Stars

16

Forks

14

Language

Python

License

MIT

Last pushed

Mar 01, 2026

Commits (30d)

0

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