Lesson 9 — Dot product & norm

Two tools behind every model: dot product and norm

A vector is just a list of numbers. The dot product of two vectors multiplies matching items and adds the results. The norm (length) of a vector is the square root of its dot product with itself — Euclidean distance from zero.

# [1, 2] · [3, 4] = 1*3 + 2*4 = 11
# norm([3, 4]) = (9 + 16) ** 0.5 = 5.0

Why it matters: a model's prediction is very often a dot product of "weights" and "features", and norms show up in similarity and regularisation. You will reuse both functions in later lessons.