Continuous Function

Domain

Linear Algebra

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

2 notebooks2 demos
Start with Vector SpacesSearch concepts
Preview the recommended route
First notebookVector Spaces

Start here. Make one prediction, then note what carries over to the next notebook.

vAAvbasis
Open first notebook
QuestionWhat carries over from Vector Spaces into Dot Product?
PredictionBefore opening the first demo, predict which quantity changes first.
Demos2 interactive demos in this domain
Before moving onSay in one sentence how the idea works before going to the next notebook.
LearnerWhich notebook should I read first?

Read the first notebook, make one prediction in its demo, then go on to the next.

Open the first notebook

Recommended route

Read these in order: each notebook comes after the notebooks in this domain that it builds on, and the label beside it gives its level. A prerequisite with no page here yet is marked as outside reading. A notebook is left out of the route only when a prerequisite has neither a page here nor a named outside reading; the full list below marks it.

  1. 01
    Vector Spaces

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

    Introductory10 mincodedemono prerequisites
  2. 02
    Dot Product

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

    Introductory12 mincodedemoafter Vector Spaces

    Dot Product builds directly on Vector Spaces.

All notebooks

Every published notebook in this domain.

Vector Spaces

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

Introductory10 mindemo

Dot Product

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

Introductory12 mindemo