# Linear spaces, the base of matrices calculations The basis for accelerating the big data models training. The last deep math posts are coming out, it’s a bit weird and difficult to read, but we need to know the basic rules of linear systems to be able to use them properly, so in this post, we explain some basic properties of linear spaces.

The last deep math posts are coming out, it’s a bit weird and difficult to read, but we need to know the basic rules of linear systems to be able to use them properly, so in this post, we explain some basic properties of linear spaces.

## Basis Components and Dimensions

By definition, a system of linearly independent vectors in a linear space _K _over a field K is called a basis for _K, _if given any x ϵ_ K _there exists a uniquely defined expansion: Expansion that should exist for every basis, self-generated.

For example, the identity matrix, created by n orthogonal vectors is a basis for the K_n _space.

When we are able to find a basis for our linear space, all the originally abstract operations become linear operations, which makes them easier to solve.

If in a linear space K, we can find n linearly independent vectors and no n+1 linearly independent vectors. Then n is called the dimension *of the space *K, which is called dimensional and the n vectors are the basis *of the space *K.

## Subspaces

Soppse that a set L of elements of a linear space Khas the following properties:

• If x ϵ Ly ϵ L, then x + y ϵ L.
• If x ϵ L *and *λ *is an element of the field *K, then λx ϵ L.

Then L is a set of elements with linear operations defined on it, this set is also a linear space and each L ϵ K *is called a *linear subspace of K.

If a basis is chosen in a subspace L, then we can always add additional vectors such as the system becomes a basis for all K.

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