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Reference implementations of algorithms used to study inner workings and mathematical logic.
Distinct from Polyglot Machine Learning References: None of the candidates cover a general, single-language reference library for statistical learning logic.
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This project is a reference collection of statistical learning algorithms built from scratch using NumPy for linear algebra and matrix operations. It serves as an educational resource for studying the mathematical foundations and inner workings of machine learning models through manual implementations. The codebase provides hand-coded implementations of both supervised and unsupervised learning. This includes classification and regression models such as support vector machines, decision trees, and Naive Bayes, as well as data clustering and pattern discovery methods like k-means and hierarchi
The project serves as a reference codebase for studying the inner workings and parameter optimization of statistical learning models.
Pygorithm is a Python algorithm library and computational logic reference. It provides a collection of standard algorithmic implementations and tools for analyzing the runtime efficiency and scaling performance of specific functions. The project enables the inspection of underlying source code for internal functions to study how logic is structured and executed. It maps specific implementations to their theoretical time and space complexity for performance analysis. The library is organized into discrete modules that support the execution of standard algorithmic patterns across variable data
Provides reference implementations of algorithms specifically to allow the study of their inner workings and mathematical logic.