Hello, hello again. So, moving forward I will be assuming you have a visual understanding

of linear transformations and how they’re represented with matrices the way I have been talking about in the last

few videos. If you think about a couple of these linear

transformations you might notice how some of them seem to

stretch space out while others squish it on in. One thing that turns out to be pretty useful

to understanding one of these transformations is to measure exactly how much it stretches

or squishes things. More specifically to measure the factor by which the given region

increases or decreases. For example look at the matrix with the columns 3, 0 and

0, 2 It scales i-hat by a factor of 3 and scales j-hat by a factor of 2 Now, if we focus our attention on the one

by one square whose bottom sits on i-hat and whose left

side sits on j-hat. After the transformation, this turns into

a 2 by 3 rectangle. Since this region started out with area 1,

and ended up with area 6 we can say the linear transformation has scaled

it’s area by a factor of 6. Compare that to a shear whose matrix has columns 1, 0 and 1, 1. Meaning, i-hat stays in place and j-hat moves

over to 1, 1. That same unit square determined by i-hat

and j-hat gets slanted and turned into a parallelogram. But, the area of that parallelogram is still

1 since it’s base and height each continue to

each have length 1. So, even though this transformation smushes

things about it seems to leave areas unchanged. At least, in the case of that one unit square. Actually though if you know how much the area of that one

single unit square changes it can tell you how any possible region in

space changes. For starters notice that whatever happens to one square

in the grid has to happen in any other square in the grid no matter the size. This follows from the fact that grid lines

remain parallel and evenly spaced. Then, any shape that is not a grid square can be approximated by grid squares really

well. With arbitrarily good approximations if you

use small enough grid squares. So, since the areas of all those tiny grid

squares are being scaled by some single amount the area of the blob as a whole will also be scaled also by that same single

amount. This very special scaling factor the factor by which a linear transformation

changes any area is called the determinant of that transformation. I’ll show how to compute the determinate of

a transformation using it’s matrix later on in the video but understanding what it is, trust me, much

more important than understanding the computation. For example the determinant of a transformation

would be 3 if that transformation increases the area

of the region by a factor of 3. The determinant of a transformation would

be 1/2 if it squishes down all areas by a factor

of 1/2. And, the determinant of a 2-D transformation

is 0 if it squishes all of space onto a line. Or, even onto a single point. Since then, the area of any region would become

0. That last example proved to be pretty important it means checking if the determinant of a

given matrix is 0 will give away if computing weather or not

the transformation associated with that matrix squishes everything into a smaller dimension. You will see in the next few videos why this is even a useful thing to think about. But for now, I just want to lay down all of

the visual intuition which, in and of itself, is a beautiful thing

to think about. Ok, I need to confess that what I’ve said

so far is not quite right. The full concept of the determinant allows

for negative values. But, what would scaling an area by a negative

amount even mean? This has to do with the idea of orientation. For example notice how this transformation gives the sensation of flipping space over. If you were thinking of 2-D space as a sheet

of paper a transformation like that one seems to turn

over that sheet onto the other side. Any transformations that do this are said

to “invert the orientation of space.” Another way to think about it is in terms

of i-hat and j-hat. Notice that in their starting positions, j-hat

is to the left of i-hat. If, after a transformation, j-hat is now on

the right of i-hat the orientation of space has been inverted. Whenever this happens whenever the orientation of space is inverted the determinant will be negative. The absolute value of the determinant though still tells you the factor by which areas

have been scaled. For example the matrix with columns 1, 1 and 2, -1 encodes a transformation that has determinant Ill just tell you -3. And what this means is that, space gets flipped over and areas are scaled by a factor of 3. So why would this idea of a negative area

scaling factor be a natural way to describe orientation flipping? Think about the seres of transformations you

get by slowly letting i-hat get closer and closer

to j-hat. As i-hat gets closer all the areas in space are getting squished

more and more meaning the determinant approaches 0. once i-hat lines up perfectly with j-hat, the determinant is 0. Then, if i-hat continues the way it was going doesn’t it kinda feel natural for the determinant

to keep decreasing into the negative numbers? So, that is the understanding of determinants

in 2 dimensions what do you think it should mean for 3 dimensions? It [determinant of 3×3 matrix] also tells

you how much a transformation scales things but this time it tells you how much volumes get scaled. Just as in 2 dimensions where this is easiest to think about by focusing

on one particular square with an area 1 and watching only what happens to it in 3 dimensions it helps to focus your attention on the specific 1 by 1 by 1 cube whose edges are resting on the basis vectors i-hat, j-hat, and k-hat. After the transformation that cube might get warped into some kind

of slanty slanty cube this shape by the way has the best name ever parallelepiped. A name made even more delightful when your

professor has a nice thick Russian accent. Since this cube starts out with a volume of

1 and the determinant gives the factor by which

any volume is scaled you can think of the determinant as simply being the volume of that parallelepiped that the cube turns into. A determinate of 0 would mean that, all of space is squished

onto something with 0 volume meaning ether a flat plane, a line, or in

the most extreme case onto a single point. Those of you who watched chapter 2 will recognize this as meaning that the columns of the matrix are linearly

dependent. Can you see why? What about negative determinants? What should that mean for 3 dimensions? One way to describe orientation in 3-D is with the right hand rule. Point the forefinger of your right hand in the direction of i-hat stick out your middle finger in the direction

of j-hat and notice how when you point your thumb up it is in the direction of k-hat. If you can still do that after the transformation orientation has not changed and the determinant is positive. Otherwise if after the transformation it only makes

since to do that with your left hand orientation has been flipped and the determinant is negative. So if you haven’t seen it before you are probably wondering by now “How do you actually compute the determinant?” For a 2 by 2 matrix with entries a, b, c,

d the formula is (a * d) – (b * c). Here’s part of an intuition for where this

formula comes from lets say the terms b and c both happed to

be 0. Then the term a tells you how much i-hat is

stretched in the x direction and the term d tells you how much j-hat is stretched in the

y direction. So, since those other terms are 0 it should make sense that a * d gives the area of the rectangle that our favorite

unit square turns into. Kinda like the 3, 0, 0, 2 example from earlier. even if only one of b or c are 0 you’ll have a parallelogram with a base a and a height d. So, the area should still be a times d. Loosely speaking if both b and c are non-0 then that b * c term tells you how much this parallelogram is stretched or squished in the diagonal direction. For those of you hungry for a more precipice

description of this b * c term here’s a helpful diagram if you would like

to pause and ponder. Now if you feel like computing determinants

by hand is something that you need to know the only way to get it down is to just practice it with a few. There’s not really that much I can say or

animate that is going to drill in the computation. This is all tripply true for 3-rd dimensional

determinants. There is a formula [for that] and if you feel like that is something you

need to know you should practice with a few matrices or you know, go watch Sal Kahn work through

a few. Honestly though I don’t think those computations fall within

the essence of linear algebra but I definitely think that knowing what the

determinate represents falls within that essence. Here’s kind of a fun question to think about

before the next video if you multiply 2 matrices together the determinant of the resulting matrix is the same as the product of the determinants

of the original two matrices if you tried to justify this with numbers it would take a really long time but see if you can explain why this makes

sense in just one sentence. Next up I’ll be relating the idea of linear transformations

covered so far to one of the areas where linear algebra is

most useful linear systems of equations see ya then!

Wowwwwww……

In Russia, the determinant calculates you

Sir ,upload video on liner combination

This video shows how college is useless and worthless

Like Francesco Bottacin, grande

I like your name for the cube better, from now on I will refer to a parallelepiped as a "slanty slanty cube" because it's just better 😆

The Chinese subtitle is awesome. There are some funny words which help me understand easily, thank you.

And now, after too many years, somehow, I finally understand determinants. Thank you so much.

I'm shocked I never came across this all the years since my undergrad linear algebra course. It was all so abstract and random. As you may have guessed my linear algebra has sucked. But better late than never.

Thank you. I have never known what the det is until now.

For those reading this and wondering why such a basic thing was never taught to them: teachers are generally towards the lower end of the intelligence spectrum compared to other professions that require college degrees.

Two minutes in and already blown away.

How can u imagine the det of a matrix size nxn? I mean how tf u imagine from dim 4 and up?

My answer:

Det(M1M2)=det(M1)det(M2) because the difference in the area of M1 and M2 is equal to the difference in the value of the determinant of M1 and the value of the determinant of M2 in that those differences are scalar and proportional.

Not sure if this would be an acceptable answer.

The Spanish subtitles have a tremendous spelling mistake. "Hambrientos" goes with H. Hope you fix that soon. Great channel anyway.

Amazing!

The square matrices of a given size whose determinant is 1 constitute a field, right?

Wait so does the determinant plot basically match the plot of cosine(x)? I'm starting to think everything is connected. And maybe that's a good thing.

The one line is as follows

As we know determinants are factors and matrix multiplication is associative

Therefore this property is proved

Pls correct me if I am wrong

You, my friend, have created possibly the most beautiful serious of videos on linear algebra that will ever exist. I'm an engineering graduate refreshing my linear algebra after years of being away, and I'm only now realizing how insufficient my previous intuition was. Thank you!

Thank you thank you thank you very much for that videos! They are incredible

After watching a couple of these videos, I somehow feel like my life would never be the same again. Thank you so very much for this!!!

6:16 параллелепипед

I divided my mind by blown and I got an answer 1

I mean MIND= BLOWN

I'm a physicist, and honestly, I really didn't know that the determinant is just a scaling factor.

Until now it was a number I never understood thoroughly.

3Blue1Brown putting math books editors out of business… I would have loved to watch this when I was 15.

Answer of the Question:

suppose a,b are sides of a two squares A,B respectively, then

det(AB) == det(A)*det(B) equals to (a * b) ^ 2 == a^2 * b^2

Earned a subscription!!!

Charles Dodgson (A.K.A. Lewis Carroll) was actually a mathematician and came up with a really easy way to calculate determinants.

i always struggle a bit in maths, but i made an attempt at answering the quiz question at the end: det(M1*M2) will describe the "overall" factor by which the original area will have been scaled. But det(M1)*det(M2) is describing the "succesive" steps/factors by which the original area has been scaled and is so eventually describes the "overall" factor too!

Is this a valid explanation? I know it's not one sentence but I hope the it makes sense

I am just blown away that I didn't know this.

It's good I do not need this in my life.

Triple scalar product happens to be the determinant.

This video pushed me to find another way to calculate determinants on 2×2 and 3×3 matrices, based purely on geometry.

On 2×2:

if a=(a1,a2), b=(b1,b2) and our matrix is:

|a1 a2|

|b1 b2|

then:

|Det| = square_root( (|a|*|b|)^2 – <a,b>^2) , whereas <a,b> is the dot product between a and b

——————————————————————————————————————————————————

On 3×3:

if a=(a1,a2,a3), b=(b1,b2,b3), c=(c1,c2,c2) and our matrix is:

|a1 a2 a3|

|b1 b2 b3|

|c1 c2 c3|

then:

|Det| = square_root( (|a|*|b|)^2 – <a,b>^2)*<c,p>/|p|

whereas p = j * ( 1 , A , (b1+b2*A)/(-b3) )

whereas A = (a1*b3-a3*b1)/(a3*b2-a2*b3) , and j is any real number

Let me know if you find this way easier than the typical 3×3 det algorithm

thats flippin awesome! i love linear algebra now

lol i'm studying algebra for 3 years and today i realized that det is the area omg :'(

This is actual teaching not mathematical gymnastics that most textbooks make of these important subjects.

Watching this series for the second time. Feeling enlightened again.

6:26 the determinant gives you the area of the parallelepiped, not the volume.

This actually made me understand Maths wtf!

3Blue1Brown should be a religion. Because this video is more magical than any gods.

2:20 the squares on the right extend beyond the blob.

Finally after many hours of flawless graphics, there is a minute mistake. So, he is human after all 🙂

Amazing

In high school, they just teach us numbers and operations on them

It's a waste of time

seriously, 5 minutes from 3Blue1Brown is better than the whole high school.

You need to do a video on the dual space, dual map, etc.

Your videos are awesome bro!

You are doing us students a great service

Nunca vi um vídeo tão didático.. 😀

A unit victor is length 1, So a 1×1 matrix is a box and the determinate of a 1×1 is 1, but the determinate of a 1×1 shear is an area greater than 1, as to of the sides of a shear have a length greater than 1. were have I gone wrong?

You are a better teacher than any I've had to pay for in college.

Students now are so lucky to have this kind of content!

Thank you so much that I'm not very familiar with linear algebra, until now.

After watching this, I start to believe there's some global conspiracy among linear algebra academic society. Seriously, I don't understand why my teacher and the authors of textbooks never mentioned this explanation of a determinant, when it is so simple and intuitive.

Так в чём проблема решить уравнение ферма?

I bet i was 'parallelepeeped' ❤

Wow! I thought about the flipping stuff myself, and can tell if a flip occurs (i.e. if i-hat moves to the left of j-hat) just by a quick glance at the matrix (basically, it happens if the diagonal coordinates i2 and j1 are both strictly greater than at least one of i1 or j2)! See my comment a couple videos ago. Playing with these ideas really does help!

To extrapolate determinants into larger contexts, requires the emotional-logic of Intuitionistic mathematics, which are about four times more complex, and capable of expressing networking systems logics. A recent Big Data meta analysis of evolution revealed the insight that metabolism actually follows how fast an organism can grow, and not the other way around. Hence, Boyle's law can extrapolated to express the modified Bayesian probabilities vanishing into indeterminacy that describe the brain, while Relativity has turned out to express the same mathematics as thermodynamics, making it possible to combine it with quantum mechanics, using the new discovery that the Golden Ratio is not entirely random, but expresses a multidimensional equation.

I have taken numerous linear algebra courses, and have a degree in maths. I have NEVER heard determinant explained this way, ever. This video is BRILLIANT! Thank you!

i got a master degree, yet nobody ever told me this

Geez! Wish I learned that insight from my linear algebra class! Thank you!

I'm in college studying maths and I just pooped my pants watching this

I’ve been solving determinants for years and never understood their conceptual properties

Nice work.

I'm a native russian speaker and I have no idea what's special if a person with russian accent will say the “parallelepiped” word. Btw its russian pronounce differs just in one letter and sounds like “parallelepeeped”.

Can someone explain what it will mean for higher dimensions i.e n>3

I have learned Linear Algebra several times. This is the first time that I'm attracted by it.

Hands down the best video for explaining the determinant of a matrix, I could not understand its meaning in university, no one explained this, but this answere it beautifully in 10min, thanks!!

How does one visualise an n x n determinant?

I cannot believe that THIS is the inner meaning of the determinant! Never explained at the university!

So is the determinant of your transformation matrix is -1 when you enter the Upside Down? 🙂

The word parallelepiped comes form the Greek "Παραλληλεπίπεδο". It's Greek to you but not to me. It means an object whose sides are parallel to each other

Was not expecting to see Dr. Eliashberg on this video! I wonder if you and I were in the same class.

How about linear differential operators like divergence, gradient etc ! Even I am a postgraduate student, I still don't understand what they represents.

What is the piano music used in the video? Thank you in advance.

this is mental dilation

In One Sentence – "If you scale the sides of any rectangle twice, its area is same as if you are multiplying the areas of rectangles formed by individual scaling."

because in both cases we multiply

cannot be more curious about how prof Eliashberg pronounces 'parallelepiped'

Nice introduction for linear algebra with "Hello hello again/LOL, oh again".

Especially on a video about tranforming spaces which are described by the things on the space, which exist on the thing they describe what it is by doing their thing.

At least from the feel i got from the previous vid.

Wow! I am 24 and all my life I didn't know is the scale of area. How come it wasn't taught to me this way?!!!😐

Muchas Gracias por compartir este video. Es super claro, y creo que son muy buenos los ejemplos.

I'm not understanding how these videos are like 10 minutes each. I think at first "cool, I have 10 minutes of awesome animations and intuition." And then, seemingly 30 seconds later, the outro music comes on and I feel like I've been swindled!

I've known for years what a determinant is, yet today I learned what a determinant IS. Wtf is wrong with my linear algebra teachers? Why did we learn so many abstract formulas and methods and not the meaning of all this?

Knowing that someday the students might leave the university and understand the esscence of linear algebra fills you with

DETERMINATION

As everybody else already pointed out, the quality of your work is outstanding and whoever has the possibility to support it should do it. A "thank you" in the comment section is not enough to repay for your effort!

You deserve a Nobel prize for making these videos..

Michael from Vsauce and you need to teach me in real life. You are the type of teacher who best fits me, dude. I am done with those teachers who when asked sth out of the syllabus, are either blank, give a completely wrong explanation cuz of confusion, or who simply say "Don't think so much on this topic. Just do this in the exam and you will fetch enough marks." These type of teachers kill a student's interest in the subject and make education a dull and sad factory process.

You would be shocked by the number of mathematicians who know HOW to figure out the determinant, but have no clue what it actually represents. Oh, BTW and am talking at the University level.

The Right Hand Rule or like Dr. Marchini at the Univ. of Memphis calls it: Physics' Gang Sign…

A random question: det(M1 *M2) = det(M1)*det(M2) = det(M2)*det(M1) = det(M2*M1).

But matrix multiplication is anti-commutative. So how can A = M1*M2 and B = M2*M1 have the same determinant?

7:44 Anno Domini – Before Christ? That's a piece of beautiful math!

A lot of people said this didnot exist in their textbooks. That is true. Because intuitive understanding is often ignored by a lot of textbooks. Intuitive understanding usually describes the origin of the math problems. I believe the founder of linear algebra must have mentioned the content in this video.

MATH IS A MODEL FOR THE REAL PHYSICAL WORLD AND IT IS USUALLY USED IN THE COMPUTATION OF PHYSICS. This is why most early mathematicians were also physicists.

3 years in a math degree and i finally understand what a determinant is x)

Am I the ONLY one who went on to Google Yakov Eliashberg..?

The Actual Answer that I feel which can be in 'one sentence' MIND YOU can be only the following

Look product of Two Matrices M1 and M2 will be always another Matrix whose determinant will a Number….which is the same as obtained from the product of their determinants which is just product of two numbers…..

For e.g Let det(M1) be A and det (M2) be B then A.B will be always C which is a number and not a matrix

hope this serves as a one sentence answer

Your videos are so informative and helpful, especially because I prefer understanding concepts of calculations instead of just using them! Thank you!

I studied at Kingston University and Middlesborough, Honours Physics Degree and computer graphics MSc. Used linear algebra extensively. Lots of 3d Animations, inverse kinematics and other complex numerical problems solved. But only now have a really understood what a determinant represents. Crazy I know but there you go. Great video. Your good, write a book, I will buy it as I am sure many others would to.

the answer to your question in one line(i hope its correct) :

if determinal is a (1×1) matrix dedicated to only one (n x n) matrix then multiplying two (1×1) matrices equals the transformation of two (nxn) matrices. there you go one line.

Why do i pay thousands of dollars to go to university?

Every linear algebra course should require this series as a primer. Having this background makes things so much clearer.

Love the video love the channel. I'm not in any classes or taking any exams. I'm learning Numpy so I can learn deep learning python and just because it's cool I"m a nerd and I never did this before. SO much to learn, you present well though, much more clearly than wikipedia on the subject, which was confusing but I also love that site. Sometimes you already have to have a good understanding to read certain pages to get a better understanding, and that was the case here. I also love your piano sounds.