Reference · Glossary
Linear algebra for ML
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When you need to understand why embeddings are vectors, how matrix multiplication powers neural nets, or why cosine similarity measures "closeness" in high dimensions.
#When to use
When you need to understand why embeddings are vectors, how matrix multiplication powers neural nets, or why cosine similarity measures "closeness" in high dimensions.
#When not to
For tabular business rules with no learned representations — a spreadsheet lookup may be enough without vectors.
#Example
import numpy as np
a = np.array([0.2, 0.8, 0.1]) # embedding for "refund policy"
b = np.array([0.3, 0.7, 0.2]) # embedding for "return rules"
similarity = np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))Key ideas: **dot product** (similarity), **matrix multiply** (layer transforms), **norms** (vector length).