In this video, I want to

tell you about a couple of special

matrix operations, called the

matrix inverse and the matrix transpose operation.

Let's start by talking about matrix

inverse, and as

usual we'll start by thinking about

how it relates to real numbers.

In the last video, I said

that the number one plays the

role of the identity in

the space of real numbers because

one times anything is equal to itself.

It turns out that real numbers

have this property that very

number have an, that

each number has an inverse,

for example, given the number

three, there exists some

number, which happens to

be three inverse so that

that number times gives you

back the identity element one.

And so to me, inverse of course this is just one third.

And given some other number,

maybe twelve there is

some number which is the

inverse of twelve written as

twelve to the minus one, or

really this is just one twelve.

So that when you multiply these two things together.

the product is equal to

the identity element one again.

Now it turns out that in

the space of real numbers, not everything has an inverse.

For example the number zero

does not have an inverse, right?

Because zero's a zero inverse, one over zero that's undefined.

Like this one over zero is not well defined.

And what we want to

do, in the rest of this

slide, is figure out what does

it mean to compute the inverse of a matrix.

Here's the idea: If

A is a n by

n matrix, and it

has an inverse, I will say

a bit more about that later, then

the inverse is going to

be written A to the

minus one and A

times this inverse, A to

the minus one, is going to

equal to A inverse times

A, is going to

give us back the identity matrix.

Okay?

Only matrices that are

m by m for some the idea of M having inverse.

So, a matrix is

M by M, this is also

called a square matrix and

it's called square because

the number of rows is equal to the number of columns.

Right and it turns out

only square matrices have inverses,

so A is a square

matrix, is m by m,

on inverse this equation over here.

Let's look at a concrete example,

so let's say I

have a matrix, three, four,

two, sixteen.

So this is a two by

two matrix, so it's

a square matrix and so this

may just could have an and

it turns out that I

happen to know the inverse

of this matrix is zero point

four, minus zero point

one, minus zero point

zero five, zero zero seven five.

And if I take this matrix

and multiply these together it

turns out what I get

is the two by

two identity matrix, I,

this is I two by two.

Okay?

And so on this slide,

you know this matrix is

the matrix A, and this matrix is the matrix A-inverse.

And it turns out

if that you are computing A

times A-inverse, it turns out

if you compute A-inverse times

A you also get back the identity matrix.

So how did I

find this inverse or how

did I come up with this inverse over here?

It turns out that sometimes

you can compute inverses by hand

but almost no one does that these days.

And it turns out there is

very good numerical software for

taking a matrix and computing its inverse.

So again, this is one of

those things where there are lots

of open source libraries that

you can link to from any

of the popular programming languages to compute inverses of matrices.

Let me show you a quick example.

How I actually computed this inverse,

and what I did was I used software called Optive.

So let me bring that up.

We will see a lot about Optive later.

Let me just quickly show you an example.

Set my matrix A to

be equal to that matrix on

the left, type three four

two sixteen, so that's my matrix A right.

This is matrix 34,

216 that I have down

here on the left.

And, the software lets me compute

the inverse of A very easily.

It's like P over A equals this.

And so, this is right,

this matrix here on my

four minus, on my one, and so on.

This given the numerical

solution to what is the

inverse of A. So let me

just write, inverse of A

equals P inverse of

A over that I

can now just verify that A

times A inverse the identity

is, type A times the

inverse of A and

the result of that is

this matrix and this is

one one on the diagonal

and essentially ten to

the minus seventeen, ten to the

minus sixteen, so Up to

numerical precision, up to

a little bit of round off

error that my computer

had in finding optimal matrices

and these numbers off the

diagonals are essentially zero

so A times the inverse is essentially the identity matrix.

Can also verify the inverse of

A times A is also

equal to the identity,

ones on the diagonals and values

that are essentially zero except

for a little bit of round

dot error on the off diagonals.

If a definition that the inverse

of a matrix is, I had

this caveat first it must

always be a square matrix, it

had this caveat, that if

A has an inverse, exactly what

matrices have an inverse

is beyond the scope of this

linear algebra for review that one

intuition you might take away

that just as the

number zero doesn't have an

inverse, it turns out

that if A is say the

matrix of all zeros, then

this matrix A also does

not have an inverse because there's

no matrix there's no A

inverse matrix so that this

matrix times some other

matrix will give you the

identity matrix so this matrix of

all zeros, and there

are a few other matrices with properties similar to this.

That also don't have an inverse.

But it turns out that

in this review I don't

want to go too deeply into what

it means matrix have an

inverse but it turns

out for our machine learning

application this shouldn't be

an issue or more precisely

for the learning algorithms where

this may be an to namely

whether or not an inverse matrix

appears and I will tell when

we get to those learning algorithms

just what it means for an

algorithm to have or not

have an inverse and how to fix it in case.

Working with matrices that don't

have inverses.

But the intuition if you

want is that you can

think of matrices as not

have an inverse that is somehow

too close to zero in some sense.

So, just to wrap

up the terminology, matrix that

don't have an inverse Sometimes called

a singular matrix or degenerate

matrix and so this

matrix over here is an

example zero zero zero matrix.

is an example of a matrix that is singular, or a matrix that is degenerate.

Finally, the last special

matrix operation I want to

tell you about is to do matrix transpose.

So suppose I have

matrix A, if I compute

the transpose of A, that's what I get here on the right.

This is a transpose which is

written and A superscript T,

and the way you compute

the transpose of a matrix is as follows.

To get a transpose I am going

to first take the first

row of A one to zero.

That becomes this first column of this transpose.

And then I'm going to take

the second row of A,

3 5 9, and that becomes the second column.

of the matrix A transpose.

And another way of

thinking about how the computer transposes

is as if you're taking this

sort of 45 degree axis

and you are mirroring or you

are flipping the matrix along that 45 degree axis.

so here's the more formal

definition of a matrix transpose.

Let's say A is a m by n matrix.

And let's let B equal A

transpose and so BA transpose like so.

Then B is going to

be a n by m matrix

with the dimensions reversed so

here we have a 2x3 matrix.

And so the transpose becomes a

3x2 matrix, and moreover,

the BIJ is equal to AJI.

So the IJ element of this

matrix B is going to be

the JI element of that

earlier matrix A. So for

example, B 1 2

is going to be equal

to, look at this

matrix, B 1 2 is going to be equal to

this element 3 1st row, 2nd column.

And that equal to this, which

is a two one, second

row first column, right, which

is equal to two and some [It should be 3]

of the example B 3

2, right, that's B

3 2 is this element 9,

and that's equal to

a two three which is

this element up here, nine.

And so that wraps up

the definition of what it

means to take the transpose

of a matrix and that

in fact concludes our linear algebra review.

So by now hopefully you know

how to add and subtract

matrices as well as

multiply them and you

also know how, what are

the definitions of the inverses

and transposes of a matrix

and these are the main operations

used in linear algebra

for this course.

In case this is the first time you are seeing this material.

I know this was a lot

of linear algebra material all presented

very quickly and it's a

lot to absorb but

if you there's no need

to memorize all the definitions

we just went through and if

you download the copy of either

these slides or of the

lecture notes from the course website.

and use either the slides or

the lecture notes as a reference

then you can always refer back

to the definitions and to figure

out what are these matrix

multiplications, transposes and so on definitions.

And the lecture notes on the course website also

has pointers to additional

resources linear algebra which

you can use to learn more about linear algebra by yourself.

And next with these new tools.

We'll be able in the next

few videos to develop more powerful

forms of linear regression that

can view of a lot

more data, a lot more

features, a lot more training

examples and later on

after the new regression we'll actually

continue using these linear

algebra tools to derive more

powerful learning algorithims as well

Inverse and Transpose

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