Linear Algebra for Computer Vision & Deep Learning
Overview
A comprehensive library and educational resource for linear algebra concepts and applications in computer vision and deep learning.
Quick Links
Getting Started
- Environment Setup - Set up your development environment
- Project Enhancement Plan - Roadmap and planned improvements
- Tool Tutorial (UV/PIXI/SageMath) - Learn about the tools we use
Reference
- Code Documentation - API and code reference
- Project Report - Current project status and findings
- Authoring Process - Guidelines for contributions
Features
- Core Linear Algebra: Matrices, vectors, eigenvalues, decompositions
- Computer Vision Applications: Homography, transformation matrices, camera calibration
- Deep Learning Tools: Layer operations, backpropagation mathematics, optimization
- Educational Resources: Tutorials, examples, and visualizations
- Production Ready: Tested, optimized implementations
Member of Miscellaneous Projects
This project is part of the Miscellaneous Projects Portfolio - a collection of experimental and educational projects.
Getting Started
For detailed setup instructions and overview, see the README.md at the repository root.
Technologies
- Python
- NumPy / SciPy
- Computer Vision algorithms
- Machine Learning frameworks
- Jupyter Notebooks
- SageMath for symbolic computation
Resources
Status: Active Development
License: MIT
Last Updated: April 2026