Linear Algebra — learning path · Lensa ML

5 steps · Learning path

Linear Algebra

Vectors, tensors, eigenvalues, and the decompositions ML relies on

1
Linear Algebra
Vectors, matrices, and the language of data
2
Tensors, Broadcasting & Reshaping
The data structures of deep learning
3
Norms & Distance Metrics
L1, L2, cosine similarity — measuring closeness in vector spaces
4
Eigenvalues & Eigenvectors
How matrices transform space — eigenvectors, eigenvalues, and decomposition
5
Singular Value Decomposition
Factor any matrix into rotation, scaling, and rotation — the Swiss army knife of linear algebra