🧮Foundations
📐
Linear Algebra
Master vectors, matrices, and transformations that form the mathematical backbone of machine learning
🌱
Beginner
BeginnerBuild intuition for vectors, matrices, and basic operations
What to Learn
- •Vectors: magnitude, direction, and operations
- •Matrix multiplication and transpose
- •Dot product and cross product
- •Identity and inverse matrices
- •Systems of linear equations
Resources
- 📚3Blue1Brown: Essence of Linear Algebra (YouTube)
- 📚Khan Academy Linear Algebra course
- 📚MIT OCW 18.06 by Gilbert Strang
🌿
Intermediate
IntermediateApply linear algebra to ML concepts
What to Learn
- •Eigenvalues and eigenvectors
- •Singular Value Decomposition (SVD)
- •Principal Component Analysis (PCA)
- •Matrix factorization techniques
- •Orthogonality and projections
Resources
- 📚Coding the Matrix (Brown University)
- 📚Linear Algebra Done Right by Axler
- 📚Implement PCA from scratch
🌳
Advanced
AdvancedDeep understanding for research applications
What to Learn
- •Tensor operations and contractions
- •Matrix calculus and gradients
- •Spectral theory
- •Low-rank approximations
- •Numerical stability in computations
Resources
- 📚Matrix Cookbook (reference)
- 📚Numerical Linear Algebra by Trefethen
- 📚Original PCA and SVD papers