Linear Algebra
i. Overview
This collection of notes provides a comprehensive overview of Linear Algebra, covering fundamental concepts essential for understanding machine learning, data science, and mathematics. Topics include:
- Vectors and Vector Spaces: Basic building blocks and their properties.
- Matrices: Operations, transformations, and solving systems of linear equations.
- Eigenvalues and Eigenvectors: Key concepts for dimensionality reduction and matrix decompositions.
- Linear Transformations: Mappings between vector spaces.
Linear Algebra is crucial for many AI algorithms and data analysis tasks. These notes summarize the core principles and applications, offering both theoretical and practical insights.
ii. Knowledge Base
| No. | Title | Description | Status | Resources |
|---|---|---|---|---|
| 01 | Introduction to Algebra (Part 1) | Vector and Matrices, Eigenvalues and Eigenvectors, Principle Component Analysis, Singular Value Decomposition | Progress | [Notes] [Code] |