**Artificial Intelligence** experts cannot live without **Linear Algebra**:

- AI make heavy use of
**Scalars** - AI make heavy use of
**Vectors** - AI make heavy use of
**Matrices** - AI 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.

## Vectors and Matrices

**Vectors** and **Matrices** are the languages of data.

With AI, 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 **1-dimensional array**.

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

## Matrices

In linear algebra, a matrix is a **2-dimensional array**.

In JavaScript, a matrix is an array with 2 indices (indexes).

### Example

var myArray = [[1,2],[3,4],[5,6]];

Learn More … (courtesy by W3Schools.com)

## Tensors

A Tensor is an **N-dimensional Matrix**.

In JavaScript, a matrix is an array with multiple indices (indexes).

**Linear Algebra** is the branch of mathematics that concerns **linear equations** (and linear maps) and their representations in **vector spaces** and through **matrices**.

*Linear algebra is central to almost all areas of mathematics.*

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