How ubaidalam01/essence-of-linear-algebra- works
This repository is a set of standalone Python lecture demonstrations for learning linear algebra, not a shared application or library. A learner runs a lecture script to see numerical examples, printed checks, and—where included—plots. The available excerpts show concepts ranging from vectors and transformations to eigenvalues and abstract vector spaces; scripts do not import one another. Other lecture files are listed in the source index but their contents were not sampled, so their topics and internal relationships are not inferred.
Main entry points: linear1.py Lecture 1 · linear2.py Basis & Span · linear10.py Cross Products · linear11.py Cross Product Duality
Components 17
Vectors & Geometry
- Lecture 1
linear1.py - Basis & Span
linear2.py - Cross Products
linear10.py - Cross Product Duality
linear11.py - Abstract Vector Spaces
linear16.py
Linear Transformations
- 2D Transform
linear3.py - Transform Composition
linear4.py - 3D Transform
linear5.py - Determinants
linear6.py - Lecture 7
linear7.py - Lecture 8
linear8.py - Lecture 9
linear9.py - Change of Basis
linear13.py
Linear Systems
- Cramer's Rule
linear12.py
Eigen Analysis
- Eigenvectors & Values
linear14.py - Eigenvalue Shortcut
linear15.py
Other components
- Learner
Connections 16
- Learner → Lecture 1: runs
linear1.py - Learner → Basis & Span: runs
linear2.py - Learner → Cross Products: runs
linear10.py - Learner → Cross Product Duality: runs
linear11.py - Learner → 2D Transform: runs
linear3.py - Learner → Transform Composition: runs
linear4.py - Learner → 3D Transform: runs
linear5.py - Learner → Determinants: runs
linear6.py - Learner → Lecture 7: runs
- Learner → Lecture 8: runs
- Learner → Lecture 9: runs
- Learner → Cramer's Rule: runs
linear12.py - Learner → Change of Basis: runs
linear13.py - Learner → Eigenvectors & Values: runs
linear14.py - Learner → Eigenvalue Shortcut: runs
linear15.py - Learner → Abstract Vector Spaces: runs
linear16.py