fengdu78/lihang-code
Lihang Code
This repository is a collection of foundational machine learning models and predictive analysis tools designed for the study of statistical learning methods. It serves as an educational resource that demonstrates the mathematical principles of classic algorithms through direct, first-principles implementation.
The project distinguishes itself by constructing models from the ground up, relying on fundamental linear algebra and calculus operations rather than high-level abstraction frameworks. Each algorithm is organized into modular, standalone scripts that mirror the sequence of mathematical derivations found in academic literature, prioritizing conceptual clarity and the exposure of internal logic over production-grade performance.
The library covers a broad range of statistical learning implementations, allowing users to prototype and execute predictive models to identify patterns within structured datasets. The source code is structured to facilitate hands-on learning, enabling the study of individual algorithms in isolation through sequential data transformation pipelines.
Features
- Machine Learning Education - Provides educational resources for studying the mathematical foundations and implementation details of statistical learning.
- Algorithm Implementations - Provides pedagogical implementations of classic machine learning algorithms to demonstrate their mathematical foundations through direct code execution.
- Machine Learning Educational Resources - Serves as an educational repository of code examples for studying classical statistical learning techniques.
- Data Science Algorithms - Offers a collection of foundational mathematical models and tools for exploring patterns in structured data.
- Machine Learning Foundations - Collects foundational machine learning models implemented from scratch to demonstrate statistical learning principles.
- Machine Learning Implementations - Enables the execution of core machine learning algorithms to identify patterns and build predictive models.
- Tabular Predictive Models - Provides a toolkit of modular scripts for predictive data modeling using fundamental mathematical operations.
- Educational Code Repositories - Structures source code to mirror academic mathematical derivations for educational clarity and study.
- Machine Learning Foundations - Code implementations for classic statistical learning methods.
- Textbooks - Aligns source code structure with academic textbook derivations to enhance educational clarity.
- Data Processing Pipelines - Executes sequential data transformation pipelines to process inputs through iterative mathematical operations.
- Machine Learning Libraries - Provides a modular library of machine learning implementations for individual study and analysis.
- Algorithm Prototypes - Facilitates the prototyping of machine learning algorithms from scratch to understand their internal mechanics.
- Standalone Scripts - Runs algorithms as independent, dependency-free scripts for clear, sequential execution.
- Linear Algebra Routines - Implements predictive models using fundamental linear algebra and calculus operations instead of high-level frameworks.
