How to organize, add and multiply matrices - Bill Shillito

544,369 views ใƒป 2013-03-04

TED-Ed


ืื ื ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ืœืžื˜ื” ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ.

00:00
Translator: Andrea McDonough Reviewer: Bedirhan Cinar
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ืชืจื’ื•ื: Shlomo Adam ืขืจื™ื›ื”: Sigal Tifferet
00:14
By now, I'm sure you know
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ืื ื™ ื‘ื˜ื•ื— ืฉืขื›ืฉื™ื• ืืชื ื›ื‘ืจ ื™ื•ื“ืขื™ื
00:15
that in just about anything you do in life,
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ืฉื›ืžืขื˜ ื‘ื›ืœ ื“ื‘ืจ ืฉืืชื ืขื•ืฉื™ื ื‘ื—ื™ื™ื,
00:17
you need numbers.
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ืืชื ื–ืงื•ืงื™ื ืœืžืกืคืจื™ื.
00:19
In particular, though,
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ืื‘ืœ ื™ืฉ ื‘ืžื™ื•ื—ื“
00:20
some fields don't just need a few numbers,
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ืชื—ื•ืžื™ื ืžืกื•ื™ืžื™ื ืฉื‘ื”ื ืœื ืฆืจื™ืš ืจืง ื›ืžื” ืžืกืคืจื™ื,
00:22
they need lots of them.
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ืืœื ื”ืžื•ืŸ ืžืกืคืจื™ื.
00:24
How do you keep track of all those numbers?
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ืื™ืš ืขื•ืงื‘ื™ื ืื—ืจื™ ื›ืœ ื”ืžืกืคืจื™ื ื”ืืœื”?
00:26
Well, mathematicians dating back
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ืžืชืžื˜ื™ืงืื™ื
00:28
as early as ancient China
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ืขื•ื“ ืžื™ืžื™ ืกื™ืŸ ื”ืขืชื™ืงื”
00:30
came up with a way to represent
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ื”ืžืฆื™ืื• ื“ืจืš ืœื™ื™ืฆื’
00:31
arrays of many numbers at once.
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ืžืขืจื›ื™ื ืฉืœ ืžืกืคืจื™ื ืจื‘ื™ื ื‘ื‘ืช-ืื—ืช.
00:34
Nowadays we call such an array a "matrix,"
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ื”ื™ื•ื ืื ื• ืงื•ืจืื™ื ืœืžืขืจืš ื›ื–ื” "ืžื˜ืจื™ืฆื”",
00:37
and many of them hanging out together, "matrices".
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ื•ื›ืฉื™ืฉ ืจื‘ื•ืช ื›ืžื•ื” - "ืžื˜ืจื™ืฆื•ืช".
00:40
Matrices are everywhere.
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ื”ืžื˜ืจื™ืฆื•ืช ื ืžืฆืื•ืช ื‘ื›ืœ ืžืงื•ื,
00:42
They are all around us,
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ื•ืขืœ ื›ืœ ืกื‘ื™ื‘ื•ืชื™ื ื•,
00:44
even now in this very room.
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ืืคื™ืœื• ืžืžืฉ ื›ืืŸ, ื‘ื—ื“ืจ.
00:47
Sorry, let's get back on track.
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ืกืœื™ื—ื”, ื‘ื•ืื• ื ื—ื–ื•ืจ ืœื ื•ืฉื.
00:49
Matrices really are everywhere, though.
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ืื‘ืœ ื”ืžื˜ืจื™ืฆื•ืช ื ืžืฆืื•ืช ื‘ืืžืช ื‘ื›ืœ ืžืงื•ื.
00:51
They are used in business,
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ืžืฉืชืžืฉื™ื ื‘ื”ืŸ ื‘ืขืกืงื™ื,
00:53
economics,
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ื‘ื›ืœื›ืœื”,
00:54
cryptography,
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00:54
physics,
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ื‘ื”ืฆืคื ื”,
ื‘ืคื™ื–ื™ืงื”,
00:56
electronics,
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ื‘ืืœืงื˜ืจื•ื ื™ืงื”
00:57
and computer graphics.
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ื•ื‘ื’ืจืคื™ืงื” ืžืžื•ื—ืฉื‘ืช.
00:59
One reason matrices are so cool
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ืื—ืช ื”ืกื™ื‘ื•ืช ืžื“ื•ืข ื”ืžื˜ืจื™ืฆื•ืช ื”ืŸ ื›ื” ืžื’ื ื™ื‘ื•ืช
01:01
is that we can pack so much information into them
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ื”ื™ื ืžืฉื•ื ืฉืื ื• ื™ื›ื•ืœื™ื ืœืžืœื ืื•ืชืŸ ื‘ื”ืžื•ืŸ ืžื™ื“ืข,
01:04
and then turn a huge series of different problems
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ื•ืื—ืจ ืœื”ืคื•ืš ืกื“ืจื” ืขื ืงื™ืช ืฉืœ ื‘ืขื™ื•ืช ืฉื•ื ื•ืช
01:06
into one single problem.
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ืœื‘ืขื™ื” ืื—ืช ื•ื™ื—ื™ื“ื”.
01:09
So, to use matrices, we need to learn how they work.
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ืื– ื›ื“ื™ ืœื”ืฉืชืžืฉ ื‘ืžื˜ืจื™ืฆื•ืช ืขืœื™ื ื• ืœื“ืขืช ืื™ืš ื”ืŸ ืคื•ืขืœื•ืช.
01:13
It turns out, you can treat matrices
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ืžืกืชื‘ืจ ืฉืืคืฉืจ ืœื”ืชื™ื™ื—ืก ืœืžื˜ืจื™ืฆื•ืช
01:15
just like regular numbers.
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ื›ืžื• ืœืžืกืคืจื™ื ืจื’ื™ืœื™ื.
01:16
You can add them,
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ืืคืฉืจ ืœื—ื‘ืจ ืื•ืชืŸ,
01:17
subtract them,
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ืœื”ืคื—ื™ืช ืื•ืชืŸ
01:18
even multiply them.
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ื•ืืคื™ืœื• ืœื”ื›ืคื™ืœ ืื•ืชืŸ.
01:19
You can't divide them,
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ืื™-ืืคืฉืจ ืœื—ืœืง ืื•ืชืŸ,
01:20
but that's a rabbit hole of its own.
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ืื‘ืœ ื–ื• ืชืขืœื•ืžื” ื ืคืจื“ืช.
01:22
Adding matrices is pretty simple.
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ื—ื™ื‘ื•ืจ ืžื˜ืจื™ืฆื•ืช ื”ื•ื ืขื ื™ื™ืŸ ืคืฉื•ื˜.
01:24
All you have to do is add the corresponding entries
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ืฆืจื™ืš ืจืง ืœื—ื‘ืจ ืืช ื”ืจืฉื•ืžื•ืช ื”ืžืงื‘ื™ืœื•ืช
01:27
in the order they come.
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ื‘ืกื“ืจ ื‘ื• ื”ืŸ ืžื•ืคื™ืขื•ืช.
01:28
So the first entries get added together,
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ื•ื›ืš, ืžื—ื‘ืจื™ื ืืช ื”ืจืฉื•ืžื•ืช ื”ืจืืฉื•ื ื•ืช,
01:30
the second entries,
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ืืช ื”ืจืฉื•ืžื•ืช ื”ืฉื ื™ื•ืช,
01:31
the third,
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01:31
all the way down.
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ืืช ื”ืฉืœื™ืฉื™ื•ืช,
ื•ื›ืŸ ื”ืœืื”, ืขื“ ื”ืกื•ืฃ.
01:33
Of course, your matrices have to be the same size,
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ื›ืžื•ื‘ืŸ ืฉื”ืžื˜ืจื™ืฆื•ืช ืฉืœื›ื ืฆืจื™ื›ื•ืช ืœื”ื™ื•ืช ื‘ืื•ืชื• ื’ื•ื“ืœ,
01:35
but that's pretty intuitive anyway.
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ืื‘ืœ ื–ื” ื“ื™ ืžื•ื‘ืŸ ืžืืœื™ื• ืžืžื™ืœื.
01:37
You can also multiply the whole matrix
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ืืคืฉืจ ื’ื ืœื”ื›ืคื™ืœ ืืช ื›ืœ ื”ืžื˜ืจื™ืฆื”
01:39
by a number, called a scalar.
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ื‘ืžืกืคืจ ื”ืงืจื•ื™ "ืกืงืœืจื™".
01:42
Just multiply every entry by that number.
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ืคืฉื•ื˜ ื”ื›ืคื™ืœื• ื›ืœ ืจืฉื•ืžื” ื‘ืžืกืคืจ ื”ื–ื”.
01:45
But wait, there's more!
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ืื‘ืœ ื–ื” ืขื•ื“ ืœื ื”ื›ืœ!
01:47
You can actually multiply one matrix by another matrix.
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ืืคืฉืจ ืžืžืฉ ืœื”ื›ืคื™ืœ ืžื˜ืจื™ืฆื” ืื—ืช ื‘ืื—ืจืช.
01:51
It's not like adding them, though,
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ืื‘ืœ ื–ื” ืœื ื›ืžื• ืœื—ื‘ืจ ื‘ื™ื ื™ื”ืŸ,
01:52
where you do it entry by entry.
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ื›ืžื• ื‘ื—ื™ื‘ื•ืจ ืฉืœ ืจืฉื•ืžื” ืื—ืจ ืจืฉื•ืžื”.
01:54
It's more unique
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ื–ื” ื™ื•ืชืจ ืžื™ื•ื—ื“
01:55
and pretty cool once you get the hang of it.
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ื•ื“ื™ ืžื’ื ื™ื‘, ืื—ืจื™ ืฉืชื•ืคืกื™ื ืืช ื”ืขื ื™ื™ืŸ.
01:57
Here's how it works.
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ื›ืš ื–ื” ืขื•ื‘ื“:
01:58
Let's say you have two matrices.
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ื ื ื™ื— ืฉื™ืฉ ืœื›ื ืฉืชื™ ืžื˜ืจื™ืฆื•ืช.
02:01
Let's make them both two by two,
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ื ื ื™ื— ืฉื”ืŸ ืฉืชื™ื™ื ืขืœ ืฉืชื™ื™ื,
02:02
meaning two rows by two columns.
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ื›ืœื•ืžืจ ืฉืชื™ ืฉื•ืจื•ืช ื•ืฉื ื™ ื˜ื•ืจื™ื.
02:05
Write the first matrix to the left
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ื›ื™ืชื‘ื• ืืช ื”ืžื˜ืจื™ืฆื” ื”ืจืืฉื•ื ื” ืžืฉืžืืœ
02:07
and the second matrix goes next to it
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ื•ืืช ื”ืžื˜ืจื™ืฆื” ื”ืฉื ื™ื” ื‘ืกืžื•ืš ืœื”
02:09
and translated up a bit,
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ื›ืฉื”ื™ื ืงืฆืช ืžื•ื’ื‘ื”ืช,
02:10
kind of like we are making a table.
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ื›ืื™ืœื• ืื ื• ื™ื•ืฆืจื™ื ื˜ื‘ืœื”.
02:12
The product we get when we multiply the matrices together
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ืžื” ืฉืžืชืงื‘ืœ ื›ืฉืื ื• ืžื›ืคื™ืœื™ื ืืช ื”ืžื˜ืจื™ืฆื•ืช
02:15
will go right between them.
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ื™ื™ื›ื ืก ื‘ื™ื ื™ื”ืŸ.
02:16
We'll also draw some gridlines to help us along.
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ื”ื‘ื” ื ืžืชื— ื›ืžื” ืงื•ื•ื™ ื˜ื‘ืœื” ื›ื“ื™ ืœื”ื™ืขื–ืจ ื‘ื”ื.
02:20
Now, look at the first row of the first matrix
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ื›ืขืช ื”ื‘ื™ื˜ื• ื‘ืฉื•ืจื” ื”ืจืืฉื•ื ื” ืฉืœ ื”ืžื˜ืจื™ืฆื” ื”ืจืืฉื•ื ื”
02:24
and the first column of the second matrix.
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ื•ื‘ื˜ื•ืจ ื”ืจืืฉื•ืŸ ืฉืœ ื”ืžื˜ืจื™ืฆื” ื”ืฉื ื™ื”.
02:26
See how there's two numbers in each?
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ืจื•ืื™ื ืฉื™ืฉ ื‘ื›ืœ ืื—ื“ ืžืžืงื•ืžื•ืช ืืœื” ืฉื ื™ ืžืกืคืจื™ื?
02:28
Multiply the first number in the row
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ื”ื›ืคื™ืœื• ืืช ื”ืžืกืคืจ ื”ืจืืฉื•ืŸ ืฉื‘ืฉื•ืจื”
02:31
by the first number in the column:
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ืขื ื”ืžืกืคืจ ื”ืจืืฉื•ืŸ ืฉื‘ื˜ื•ืจ:
02:33
1 times 2 is 2.
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1 ื›ืคื•ืœ 2 ืฉื•ื•ื” 2.
02:35
Now do the next ones:
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ื›ืขืช ื”ื›ืคื™ืœื• ืืช ื”ื‘ืื™ื ื‘ืชื•ืจ:
02:37
3 times 3 is 9.
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3 ื›ืคื•ืœ 3 ืฉื•ื•ื” 9.
02:39
Now add them up:
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ื›ืขืช ื—ื‘ืจื• ืื•ืชื:
02:41
2 plus 9 is 11.
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2 ื•ืขื•ื“ 9 ืฉื•ื•ื” 11.
02:43
Let's put that number in the top-left position
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ื ืฆื™ื‘ ืืช ื”ืžืกืคืจ ื”ื–ื” ื‘ืžืงื•ื ื”ืฉืžืืœื™ ื”ืขืœื™ื•ืŸ
02:46
so that it matches up with the rows and columns
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ื›ื“ื™ ืฉื™ืชืื™ื ืžื‘ื—ื™ื ืช ื”ืฉื•ืจื•ืช ื•ื”ื˜ื•ืจื™ื
02:48
we used to get it.
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ื‘ื”ืŸ ื”ืฉืชืžืฉื ื• ื›ื“ื™ ืœืงื‘ืœื•.
02:49
See how that works?
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ืจื•ืื™ื ืื™ืš ื–ื” ืขื•ื‘ื“?
02:50
You can do the same thing to get the other entries.
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ืืชื ื™ื›ื•ืœื™ื ืœืขืฉื•ืช ื›ืš ื›ื“ื™ ืœืงื‘ืœ ืืช ื™ืชืจ ื”ืจืฉื•ืžื•ืช.
02:53
-4 plus 0 is -4.
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4- ื•ืขื•ื“ 0 ื”ื 4-.
02:57
4 plus -3 is 1.
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4 ื•ืขื•ื“ 3- ืฉื•ื•ื” 1.
03:01
-8 plus 0 is -8.
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8- ื•ืขื•ื“ 0 ืฉื•ื•ื” 8-.
03:05
So, here's your answer.
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ืื– ื–ื”ื• ื”ืคืชืจื•ืŸ.
03:07
Not all that bad, is it?
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ื‘ื›ืœืœ ืœื ืจืข, ื ื›ื•ืŸ?
03:09
There's one catch, though.
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ื™ืฉ ืจืง ืžื™ืœื›ื•ื“ ืื—ื“.
03:10
Just like with addition,
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ืžืžืฉ ื›ืžื• ื”ื—ื™ื‘ื•ืจ ืขืฆืžื•,
03:12
your matrices have to be the right size.
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ื’ื ื”ืžื˜ืจื™ืฆื” ืฆืจื™ื›ื” ืœื”ื™ื•ืช ื‘ื’ื•ื“ืœ ื”ื ื›ื•ืŸ.
03:15
Look at these two matrices.
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ื”ื‘ื™ื˜ื• ื‘ืฉืชื™ ื”ืžื˜ืจื™ืฆื•ืช ื”ืืœื”.
03:17
2 times 8 is 16.
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2 ื›ืคื•ืœ 8 ืฉื•ื•ื” 16.
03:20
3 times 4 is 12.
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3 ื›ืคื•ืœ 4 ืฉื•ื•ื” 12.
03:23
3 times
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3 ื›ืคื•ืœ
03:25
wait a minute,
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ืจื’ืข,
03:26
there are no more rows in the second matrix.
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ื‘ืžื˜ืจื™ืฆื” ื”ืฉื ื™ื” ืื™ืŸ ื™ื•ืชืจ ืฉื•ืจื•ืช.
03:28
We ran out of room.
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ื ื’ืžืจ ืœื ื• ื”ืžืงื•ื.
03:30
So, these matrices can't be multiplied.
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ืœื›ืŸ ื”ืžื˜ืจื™ืฆื•ืช ื”ืืœื” ืœื ื ื™ืชื ื•ืช ืœื”ื›ืคืœื”.
03:33
The number of columns in the first matrix
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ืžืกืคืจ ื”ื˜ื•ืจื™ื ื‘ืžื˜ืจื™ืฆื” ื”ืจืืฉื•ื ื”
03:35
has to be the same as the number of rows in the second matrix.
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ืฆืจื™ืš ืœื”ื™ื•ืช ื–ื”ื” ืœืžืกืคืจ ื”ืฉื•ืจื•ืช ื‘ืžื˜ืจื™ืฆื” ื”ืฉื ื™ื”.
03:39
As long as you're careful
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ืืš ื›ืœ ืขื•ื“ ืชืงืคื™ื“ื•
03:40
to match up your dimensions right, though,
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ืฉืžื™ื“ื•ืช ื”ืžื˜ืจื™ืฆื•ืช ืชื”ื™ื™ื ื” ื–ื”ื•ืช,
03:42
it's pretty easy.
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ื–ื” ื“ื™ ืงืœ.
03:43
Understanding matrix multiplication
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ื”ื‘ื ืช ื”ื›ืคืœืชืŸ ืฉืœ ืžื˜ืจื™ืฆื•ืช
03:45
is just the beginning, by the way.
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ื”ื™ื ืจืง ื”ื”ืชื—ืœื”, ืื’ื‘.
03:46
There's so much you can do with them.
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ื™ืฉ ื”ืจื‘ื” ืžืื“ ื“ื‘ืจื™ื ืฉืืคืฉืจ ืœืขืฉื•ืช ืื™ืชืŸ.
03:48
For example, let's say you want
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ืœื“ื•ื’ืžื”, ื ื ื™ื— ืฉืืชื ืจื•ืฆื™ื
03:50
to encrypt a secret message.
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ืœื”ืฆืคื™ืŸ ื”ื•ื“ืขื” ืกื•ื“ื™ืช.
03:52
Let's say it's "Math rules".
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ื ื ื™ื— ืฉื”ื”ื•ื“ืขื” ื”ื™ื "ืื™ืŸ ื›ืžื• ื”ืžืชืžื˜ื™ืงื”".
03:54
Though, why anybody would want to keep this a secret
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ืื ื›ื™, ืœืžื” ืžื™ืฉื”ื• ื™ืจืฆื” ืœืฉืžื•ืจ ื–ืืช ื‘ืกื•ื“?
03:56
is beyond me.
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ืื™ืŸ ืœื™ ืžื•ืฉื’.
03:58
Letting numbers stand for letters,
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ืื ื ืฉืชืžืฉ ื‘ืžืกืคืจื™ื ื›ื“ื™ ืœื™ื™ืฆื’ ืืช ื”ืื•ืชื™ื•ืช,
03:59
you can put the numbers in a matrix
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ืืคืฉืจ ืœื”ืฆื™ื‘ ืืช ื”ืžืกืคืจื™ื ื‘ืžื˜ืจื™ืฆื”
04:01
and then an encryption key in another.
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ื•ืฆื•ืคืŸ ื”ืฆืคื ื” ื‘ืžื˜ืจื™ืฆื” ืฉื ื™ื”.
04:03
Multiply them together
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ืœื”ื›ืคื™ืœ ืื•ืชืŸ ื–ื• ื‘ื–ื•
04:04
and you've got a new encoded matrix.
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ื•ืงื™ื‘ืœืชื ืžื˜ืจื™ืฆื” ืžื•ืฆืคื ืช ื—ื“ืฉื”.
04:07
The only way to decode the new matrix
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ื”ื“ืจืš ื”ื™ื—ื™ื“ื” ืœืคืขื ื— ืืช ื”ืžื˜ืจืฆื™ื” ื”ื—ื“ืฉื”
04:09
and read the message
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ื•ืœืงืจื•ื ืืช ื”ื”ื•ื“ืขื”
04:10
is to have the key,
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ื”ื™ื ื‘ืขื–ืจืช ื”ืฆื•ืคืŸ,
04:12
that second matrix.
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ืื•ืชื” ืžื˜ืจื™ืฆื” ืฉื ื™ื”.
04:13
There's even a branch of mathematics
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ื™ืฉ ืืคื™ืœื• ืขื ืฃ ื‘ืžืชืžื˜ื™ืงื”
04:15
that uses matrices constantly,
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ืฉืžืฉืชืžืฉ ื›ืœ ื”ื–ืžืŸ ื‘ืžื˜ืจื™ืฆื•ืช,
04:17
called Linear Algebra.
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ื•ื”ื•ื ืงืจื•ื™ "ืืœื’ื‘ืจื” ืœื™ื ืืจื™ืช".
04:18
If you ever get a chance to study Linear Algebra,
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ืื ืื™-ืคืขื ื™ื–ื“ืžืŸ ืœื›ื ืœืœืžื•ื“ ืืœื’ื‘ืจื” ืœื™ื ืืจื™ืช,
04:21
do it, it's pretty awesome.
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ืขืฉื• ื–ืืช, ื–ื” ื“ื™ ืžื“ื”ื™ื.
04:22
But just remember,
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ืจืง ื–ื™ื›ืจื•,
04:24
once you know how to use matrices,
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ืฉื›ืืฉืจ ืืชื ื›ื‘ืจ ื™ื•ื“ืขื™ื ืื™ืš ืœื”ืฉืชืžืฉ ื‘ืžื˜ืจื™ืฆื•ืช,
04:26
you can do pretty much anything.
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ืืชื ื™ื›ื•ืœื™ื ืœืขืฉื•ืช ื‘ืขืฆื ื”ื›ืœ.
ืขืœ ืืชืจ ื–ื”

ืืชืจ ื–ื” ื™ืฆื™ื’ ื‘ืคื ื™ื›ื ืกืจื˜ื•ื ื™ YouTube ื”ืžื•ืขื™ืœื™ื ืœืœื™ืžื•ื“ ืื ื’ืœื™ืช. ืชื•ื›ืœื• ืœืจืื•ืช ืฉื™ืขื•ืจื™ ืื ื’ืœื™ืช ื”ืžื•ืขื‘ืจื™ื ืขืœ ื™ื“ื™ ืžื•ืจื™ื ืžื”ืฉื•ืจื” ื”ืจืืฉื•ื ื” ืžืจื—ื‘ื™ ื”ืขื•ืœื. ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ื”ืžื•ืฆื’ื•ืช ื‘ื›ืœ ื“ืฃ ื•ื™ื“ืื• ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ ืžืฉื. ื”ื›ืชื•ื‘ื™ื•ืช ื’ื•ืœืœื•ืช ื‘ืกื ื›ืจื•ืŸ ืขื ื”ืคืขืœืช ื”ื•ื•ื™ื“ืื•. ืื ื™ืฉ ืœืš ื”ืขืจื•ืช ืื• ื‘ืงืฉื•ืช, ืื ื ืฆื•ืจ ืื™ืชื ื• ืงืฉืจ ื‘ืืžืฆืขื•ืช ื˜ื•ืคืก ื™ืฆื™ืจืช ืงืฉืจ ื–ื”.

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