Domain Neighborhood

Linear Algebra

Vectors, matrices, and linear maps: the language of representations, optimization, and modern deep learning.

2 concepts2 published2 demos

Start with Vector Spaces Search Atlas

Selected domain objectVector Spaces

Start here. Predict once, then carry the invariant forward.

vAAvbasis

Open first notebook

QuestionWhich invariant should survive into Dot Product?

PredictionBefore the first demo, predict which variable moves first.

Evidence2 demo witnesses in this domain

InvariantName the mechanism before continuing the route.

Learner lensWhat makes this domain feel navigable?

Stabilize the first mechanism, make one prediction, then move one node forward.

Take this lens

Recommended Route

Start here, then follow the prerequisites forward.

This sequence is ordered for learning rather than inventory. Published notebooks with an unavailable prerequisite—or a same-domain route step that depends on one—are labeled in the full inventory instead of being presented as ready steps.

01
Vector Spaces
A vector space is a set of objects you can add and scale, where those operations behave consistently.
10 mincodedemoentry point
Entry point: build the first mental model here.
02
Dot Product
The dot product measures alignment: it connects angles, lengths, and projections, and underlies cosine similarity in ML.
12 mincodedemoafter Vector Spaces
Why this follows: Dot Product uses Vector Spaces directly.

All Published Notebooks

Browse the territory.

Vector Spaces

A vector space is a set of objects you can add and scale, where those operations behave consistently.

Level 210 mindemo

Dot Product

The dot product measures alignment: it connects angles, lengths, and projections, and underlies cosine similarity in ML.

Level 212 mindemo