## Master elements of linear algebra, start with easy and visual explanations of basic concepts

Often the primary difficulty one faces when one desires to begin one’s journey into the world of machine learning is having to grasp math concepts. Sometimes this could be difficult for those who shouldn’t have a solid background in subjects equivalent to linear algebra, statistics, probability, optimization theory, or others. 🤔💭🔢✖️🧮

In this text then, I would love to start out by givingthat are essential before delving into the world of Machine Learning. Obviously, this text just isn’t meant to be exhaustive there’s loads to find out about this subject, but possibly it may possibly be a primary approach to tackling this subject!

- Introduction
- What’s a vector?
- Easy Vector Operations
- Projections
- Basis, Vector Space and Linear Indipendence
- Matrices and Solving Equations

## Introduction

allows us to , especially problems which might be quite common in data science.

Assume we go to the market to purchase 3 avocados, and 4 broccoli and pay $8. The subsequent day we buy 11 avocados and a pair of broccoli and pay $12.

Now we would like to learn how much a single avocado and a single broccoli cost. We’ve got to resolve the next expressions concurrently.

One other typical problem is to for it . So suppose we already know what sort of function we’d like to make use of, but this . We would like to .

Let’s for instance call * = param1 *and * = param2*.

Normally, in Machine Learning, we would like to to seek out at the top some good curve that matches our data.

Let’s say that , while . We normally say that we would like to seek out those parameters [µ, θ] as a way to , so find the curve which is as closest as possible to the green one.

Let’s see how linear algebra might help us with these problems!

## What’s a vector?

A in physics is a that has a direction an indication and a magnitude. So it is usually represented visually with an arrow.

Often in . In reality, you’ll hear again and again the term list as a substitute of vector. On this conception, the vector is nothing greater than a that we will use to represent anything.

Suppose we would like to represent houses in keeping with 3 of their properties:

1. The variety of rooms

2. The variety of bathrooms

3. Square meters

For instance, within the image above we now have two vectors. The primary represents a house with 4 bedrooms, 2 bathrooms and 85 square meters. The second, however, represents a house with 3 rooms, 1 bathroom and 60 square meters.

In fact, if we’re taken with other properties of the home we will create a for much longer vector. On this case, we’ll say that the vector as a substitute of getting 3 dimensions can have *n dimensions.*!

## Easy Vector Operations

There are operations we will perform with vectors, the only of that are actually addition between two vectors, and multiplication of a vector by a ().

To . That’s, you draw vectors parallel to those we would like so as to add after which draw the diagonal. The diagonal can be the resulting vector of the addition. Imagine me, it is far easier to grasp this by looking directly at the next example.

While . See the next example.

## Modulus and Inner Product

. For instance, allow us to take as.

Now we define a latest vector *r*, which , that’s, from the purpose where *i* and *j* meet, and which is *a* times longer than *i,* and *b* times longer than j.

More commonly , in this manner we will discover various vectors in a .

Now we’re able to define a latest operation, the ,could be derived from its coordinates and is defined as follows.

The however is one other operation with which given two vectors, it multiplies all their components and returns the sum.

The inner product has some properties that could be useful in some cases :

- commutative :
*r*s = s*r* - distributive over addition :
*r*(s*t) = r*s + r*t* - associative over scalar multiplication:
*r*(a*s) = a*(r*s) where a is a scalar*

Notice that for those who compute the inner product of a vector per itself, you’re going to get its modulus squared!

## Cosine (dot) Product

Thus far we now have only seen a mathematical definition of the inner product based on the coordinates of vectors. Now allow us to of it. Allow us to create 3 vectors *r, s* and their difference *r-s*, in order to form a triangle with 3 sides *a,b,c*.

We all know from our highschool days that .

But then we will derive from the above that:

So the comprised angle has a robust effect on the results of this operation. In reality in some special cases where the angle is 0°, 90°, and 180° we can have that the cosine can be 0,1,-1 respectively. And so we can have computer graphics on this operation. So for instance,.

## Projection

Let’s consider two vectors r and s. These two vectors are close to one another from one side and make an angle *θ* in between them. .

There are 2 basics projection operations:

- : gives us the magnitude of the projection
- : gives us the projection vector itself

## Changing Basis

Changing basis in linear algebra refers back to the , . A

We’ve got seen, for instance, that in two dimensions each vector could be represented as a sum of two basis vectors [0,1] and [1,0]. These two vectors are the idea of our space. But . Let’s see how.

Within the image above, I even have two bases. The bottom (e1, e2), and the bottom (b1,b2). As well as, I even have a vector r (in red). This vector has coordinates [3,4] when expressed by way of (e1,e2) which is the bottom we’ve all the time utilized by default. But how do its coordinates grow to be when expressed by way of (b1,b2)?

To seek out these coordinates we’d like to go by steps. First, we’d like to seek out the projections of the vector r onto the vectors of the brand new base (b1,b2).

It’s easy to see that the sum of those projections we created is just r.

r = p1 + p2.

Moreover, as a way to change the idea, , meaning that the vectors are at 90 degrees to one another, in order that they can define the entire space.

Now we go on to , with the formula we saw within the previous chapter.

The worth circled in red within the vector projection will give us the coordinate of the brand new vector r expressed in base b : (b1,b2) as a substitute of e : (e1,e2).

To ascertain that the calculations are right we’d like to ascertain that the sum of the projections is just r in base e:(e1,e2).

## Basis, Vector Space and Linear Indipendence

We’ve got already seen and talked about basis. But let’s define more precisely what a vector basis is in a vector space.

that:

- (linearly independent)
- : the space is n-dimensional

The primary point signifies that if, for instance, I even have 3 vectors *a,b,c *forming a basis, meaning there isn’t a approach to add these vectors together and multiply them by scalars and get zero!

If I denote by *x y *and *z* any three scalars (two numbers), it signifies that :

(obviously excluding the trivial case where x = y = z = 0). On this case, we’ll say that the vectors are linearly independent.

This implies, for instance, that . It signifies that .

While the second point signifies that I can multiply these vectors by scalars and sum them together to get any possible vectors in a three-dimensional space. .

## Matrices and solving simultaneous equations

By now you ought to be pretty good at handling vectors and doing operations with them. But what are they used for in real life? We saw to start with that one among our goals was to resolve multiple equations together concurrently, for instance, to work out the costs of vegetables on the supermarket.

But now that we all know the vectors we will rewrite these equations in a less complicated way. We put the vectors of coefficients [2,10] and [3,1] next to one another in forming a matrix (set of vectors). Then we can have the vector of unknowns [a,b] and at last the result [8,3].

Now chances are you’ll ask whether this latest type of writing the issue is actually higher or not. It is vitally easy. Just multiply each row of the matrix by the vector. In case we had a multiplication between two matrices we’d must multiply each row of the primary matrix by each column of the second matrix.

But then we may also say that

So our initial problem could be interpreted as follows, “What’s the unique vector [a,b] on which the transformation leads to [8,3]?”

In this manner, . Plus operations with matrices have the next properties that could be very useful.

Given A(r) = r2 where A is a matrix and r, r2 are each scalar:

*A(nr) = ns*where*n*is a scalar*A(r+s) = A(r) + A(s)*where*s*is a vector

## Matrices and space transformations

To know the results of a matrix then we will see how they transform the vectors to which they’re applied. Specifically, we would see what’s the impact of a matrix when applied on the eigenbasis.

If we now have a 2×2 matrix and we’re in an area in two dimensions, the primary column of the matrix will tell us what the effect can be on the vector e1 = [1,0] and the second column as a substitute will tell us what the effect can be on the vector e1 = [0,2].

We then see the effect of some known matrices. These transformations are sometimes useful in Machine Learning for data augmentation on images, you may stretch or shrink those images for instance.

. So if we now have two transformations represented by the matrices A1 and A2 we will apply them consecutively A2(A1(vector)).

But that is different from applying them inversely i.e. A1(A2(vector)). That’s the reason

In this primary a part of my articles on linear algebra, you need to have understood why this subject is so essential for Machine Learning and maybe you’ve learned basic concepts quickly and intuitively.

You understand what a vector and a matrix are, the way to represent these entities in a vector space and the way to do operations with these elements. Follow along so that you don’t miss the continuation of this text! 😊

*Marcello Politi*