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Linear Algebra

ML make heavy use of

Scalars

ML make heavy use of

Vectors

ML make heavy use of

Matrices

ML make heavy use of

Tensors

The purpose of this chapter is to highlight the parts of linear algebra that is used in data science projects like machine learning and deep learning.

Scalar

Vector(s)

Matrix

Tensor

Vectors and Matrices

Vectors and

Matrices are the languages of data. With ML, most things are done with vectors and matrices. With vectors and matrices, you can Discover Secrets.

Scalars

In linear algebra, a scalar is a single number. In JavaScript it can be written like a constant or a variable:

const myScalar = 1;
let x = 1;
var y = 1;

Vectors

In linear algebra, a vector is an array of numbers. In JavaScript, it can be written as an array:

const myArray = [50,60,70,80,90,100,110,120,130,140,150];
myArray.length;   // the length of myArray is 11

An array can have multiple dimensions, but a vector is a

Formula

1 - dimensional array.

A vector can be written in many ways. The most common are: v =

or: v =

The image to the left is a

Vector.

The

Length shows the

Magnitude.

The

Arrow shows the

Direction. Learn More ...

Matrices

In linear algebra, a matrix is a

Formula

2 - dimensional array.

C =

In JavaScript, a matrix is typically represented as a two-dimensional (2D) array, meaning an array of arrays. Each inner array represents a row, and each element within those arrays represents a column.