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Basis

As explained in Math and Architectures of Deep Learning

A vector in a spanning set that provides a coordinate direction for representing every vector in the space.

A basis is a set of vectors that spans a vector space, allowing every vector in that space to be expressed as a linear combination of the basis vectors. Geometrically, basis vectors provide coordinate directions for describing points in high-dimensional spaces. section 2.9 section 2.9.3

A basis has several defining properties:

  • Exactly n basis vectors are needed to span an n-dimensional space such as ℝⁿ. section 2.9.3
  • Basis vectors need not be orthogonal; any linearly independent pair forms a basis for ℝ². section 2.9.3
  • The familiar Cartesian coordinate axes are one basis, but bases are not unique. section 2.9.3

Coordinate directions in ℝ²

The two standard coordinate-direction vectors form a basis for ℝ²: every two-dimensional vector can be represented as a linear combination of them. Replacing them with any two linearly independent vectors gives another basis, even when the directions are not perpendicular. section 2.9.3

How Math and Architectures of Deep Learning covers it

How deep it goes
Defines spanning, dimensionality, nonuniqueness, orthogonality, and linear independence, while stopping short of computational implementation.
Examples
Uses Cartesian coordinate axes in ℝ², arbitrary independent pairs, and the general ℝⁿ dimensionality rule.
What you can do after
Evaluate whether vectors span a space and understand how their coordinates represent every vector in that space.
How it connects
Connects basis vectors to vector spaces, linear combinations, linear independence, coordinate axes, orthogonality, and closure.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Math and Architectures of Deep Learning. The section links point to the parts of the book it draws on.