Track 1 · Path · 5 explainers
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