Linear Algebra
The language of data, weights, and transformations. Every forward pass, every embedding, every optimization step is linear algebra under the hood.
vectors
dot product
matrices
eigenvalues
SVD
PCA
norms
rank
orthogonality
Topic overview
Treat vectors, dot products, matrices, eigenvectors, SVD, and PCA as the shared language behind features, embeddings, transformations, and compression.
Core concepts
Focus on vector shape, norms, projections, matrix multiplication, basis changes, rank, eigen decomposition, singular values, and geometric intuition.
Why it matters
Most ML systems are matrix operations at scale: model layers, embeddings, similarity search, PCA, and attention all depend on linear algebra.
Interview relevance
Know how to explain dimensions, similarity, projections, PCA, and why matrix operations make model computation efficient.