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- [Instructor] In a previous video,
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we saw that if you have a system
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of three equations with
three unknowns like this,
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you can represent it as
a matrix vector equation,
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where this matrix right over here
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is a three-by-three matrix.
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That is essentially a coefficient matrix.
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It has all of the coefficients of the Xs,
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the Ys, and the Zs as its various columns.
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and then you're going to
multiply that times this vector,
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which is really the vector
of the unknown variables,
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and this is a three-by-one vector.
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And then you would result
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in this other three-by-one
vector, which is a vector
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that contains these constant
terms right over here.
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What we're gonna do in
this video is recognize
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that you can generalize this phenomenon.
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It's not just true with a system
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of three equations with three unknowns.
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It actually generalizes to
N equations with N unknowns.
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But just to appreciate that
that is indeed the case,
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let us look at a system of two
equations with two unknowns.
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So let's say you had 2x
plus y is equal to nine,
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and we had 3x minus y is equal to five.
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I encourage you, pause this video
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and think about how that
would be represented
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as a matrix vector equation.
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All right, now let's
work on this together.
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So this is a system of two
equations with two unknowns.
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So the matrix that represents
the coefficients is going
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to be a two-by-two matrix
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and then that's going to be multiplied
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by a vector that represents
the unknown variables.
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We have two unknown variables over here.
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So this is going to be
a two-by-one vector,
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and then that's going
to be equal to a vector
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that represents the constants
on the right-hand side,
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and obviously we have two of those.
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So that's going to be a
two-by-one vector as well.
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And then we can do exactly what we did
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in that previous example
in a previous video.
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The coefficients on the
X terms, two and three,
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and then we have the
coefficients on the Y terms.
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This would be a positive one
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and then this would be a negative one.
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And then we multiply it times the vector
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of the variables, X, Y,
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and then last but not least,
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you have this nine and this
five over here, nine and five.
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And I encourage you and multiply this out.
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Multiply this matrix times this vector.
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And when you do that and you
still set up this equality,
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you're going to see that
it essentially turns
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into this exact same system
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of two equations and two unknowns.
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Now, what's interesting about this is
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that we see a generalizable form.
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In general, you can represent a system
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of N equations and N unknowns in the form.
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Sum N-by-N matrix A,
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N by N,
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times sum N-by-one vector X.
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This isn't just the variable X.
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This is a vector X that
has N dimensions to it.
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So times sum N-by-one vector X is going
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to be equal to sum N-by-one vector B.
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These are the letters that
people use by convention.
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This is going to be N by one.
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And so you can see in
these different scenarios.
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In that first one, this is
a three-by-three matrix.
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We could call that A,
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and then we could call this the vector X,
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and then we could call this the vector B.
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Now in that second scenario,
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we could call this the matrix A,
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we could call this the vector X,
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and then we could call this the vector B,
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but we can generalize
that to N dimensions.
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And as I talked about
in the previous video,
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what's interesting about this
is you could think about,
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for example, in this
system of two equations
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with two unknowns, as all
right, I have a line here,
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I have a line here,
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and X and Y represent the
intersection of those lines.
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But when you represent it this way,
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you could also imagine it as saying,
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okay, I have some unknown
vector in the coordinate plane
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and I'm transforming it using this matrix
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to get this vector nine five.
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And so I have to figure out what vector,
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when transformed in this
way, gets us to nine five,
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and we also thought about it
in the three-by-three case.
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What three-dimensional vector,
when transformed in this way,
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gets us to this vector right over here?
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And so that hints,
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that foreshadows where
we might be able to go.
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If we can unwind this
transformation somehow,
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then we can figure out what
these unknown vectors are.
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And if we can do it in two
dimensions or three dimensions,
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why not be able to do it in N dimensions?
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Which you'll see is actually very useful
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if you ever become a data scientist,
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or you go into computer science,
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or if you go into computer
graphics of some kind.