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EECS 16B Lectures - Shared screen with speaker view
mjayasur
38:22
pogchamp
gghosal
43:02
In this case are the x values in the D matrix also from the y measurements?
mjayasur
43:41
Is this only for discrete systems?
gghosal
45:54
Ok thank you for the clarification
mjayasur
46:07
Thanks!
Balaji
49:21
How come e isn't anywhere in the expression for p-hat?
mjayasur
49:31
since we are adding in the error vector for a given timestep, shouldn’t we be using x hat instead of plain x vector? I’m sort of confused how the e error vector is coming into play.
Subham Dikhit
50:06
Kind of to add on, then which form for the error is right: the change he just made, or the original form?
Seth SANDERS
51:16
Questions in a moment
mjayasur
51:28
Thanks!
Vainavi Viswanath
51:55
I’m not sure about this but I thought B has dimensions nxm so wouldn’t the transpose have mxn dimensions?
Alberto Checcone
52:18
it does
James Shi
52:46
I think the error is something we’re trying to minimize like with least squares
Balaji
53:04
So the error is y-hat - y?
Calvin Yan
53:30
Should be
Ashwin Rammohan
55:09
why does I go from 1 to R
Ashwin Rammohan
55:21
aren't there n columns?
Aidan Higginbotham
55:32
@Ashwin i think that's a n
Calvin Yan
56:01
No, the number of states n and the number of samples R don’t have to be the same
Aidan Higginbotham
56:24
Oh ignore me then lol
dylanbrater
56:59
If both u and x are n long, then that multiplication doesn’t make sense, bc the two matrices have a n+m height whereas x + u is n+n height
Ashwin Rammohan
57:05
@calvin if he's saying n separate equations, shouldn't i go from 1 to n?
Ashwin Rammohan
57:13
what is R, the number of samples?
Vanshaj Singhania
57:14
wait im pretty sure that's an n @Ashwin @Calvin
Ashwin Rammohan
57:26
@vanshaj ok yeah makes sense
Ranelle
57:30
Doesn’t e increase as we approach the projection of y?
Balaji
57:34
YEah, thanks
Calvin Yan
58:49
Oh I see that now
AJ
01:00:33
So this is finding the best estimate of A and B?
Francisco Galvan
01:00:54
oh, thanks! i actually that was the Reals, good to go
Ayush Sharma
01:01:00
@AJ, yup, I believe that's correct.
AJ
01:01:23
But don’t we get it in some weird transpose form? How do we get A and B back?
Francisco Galvan
01:02:01
i typoed, my mistake, i understand its "n"
Vanshaj Singhania
01:02:12
I think you can reconstruct the matrix with AT and BT
Vanshaj Singhania
01:02:36
and then you know the dimensions n and m so you can visually separate AT from BT and transpose them back @AI
Stephen
01:02:41
so dimension of y is n x L and dimension of D is (n+m )x L?
Stephen
01:02:56
dimension of p is n x (n + m)?
dylanbrater
01:03:42
1
mjayasur
01:03:43
1
Balaji
01:03:43
1?
Vanshaj Singhania
01:03:44
1
Francisco Galvan
01:03:44
1?
Ashwin Rammohan
01:03:45
1
Ranelle
01:03:45
1!
Jake Whinnery
01:04:00
Dimension of the column space
Yimo Xu
01:04:01
The dimension of column space
Balaji
01:04:06
dim range (A)
Jake Whinnery
01:04:08
1
Hamza Kamran Khawaja
01:04:13
3
Ranelle
01:04:28
4! Jk lol
Van Chung
01:05:37
I just want to make sure that Rank is the number of independent column or independent row?
Ranelle
01:05:42
Both.
Kunaal
01:05:47
row rank = column rank
Alberto Checcone
01:05:52
yes. it's the dimension space of all possible outputs
James Shi
01:06:17
[1 2 3] [1 1 1 1 1]
Ranelle
01:06:18
P x Q such that it’s equal to A?
Lily Yang
01:06:21
p [1 2 3]; qt = [1 1 1 1 1]
ayang
01:06:22
[1;2;3] [1,1,1,1,1]
Terrance Li
01:06:22
p = <1,2,3> q = <1,1,1,1,1>
Ranelle
01:06:25
Q^T*
mjayasur
01:06:30
P = [1, 2, 3] qT = [1, 1, 1, 1, 1]
James Shi
01:06:37
no
ayang
01:06:43
l,mao
felixyu
01:06:46
xD
Lucas Huang
01:06:54
the disrespect
Stephen
01:07:04
yes
Lily Yang
01:07:04
any multiples
James Shi
01:07:10
scale p by 2, q by 1/2
Stephen
01:07:13
any scaled version should be fine, right?
ayang
01:07:15
many answers
Vanshaj Singhania
01:07:16
[2 4 6] [.5 .5 .5 .5 .5]
Jake Whinnery
01:07:17
246 and .5.5.5.5.5
mjayasur
01:07:19
yeahz
Ranelle
01:07:23
Any real scalar multiple of Q^T
Calvin Yan
01:10:39
Wouldn’t q^t have to be normalized relative to the first value of the first row?
Vanshaj Singhania
01:11:07
yeah
Yimo Xu
01:13:55
Germany
mjayasur
01:13:56
yeah
Aidan Higginbotham
01:13:59
yes
Ranelle
01:13:59
Deutshland
Lily Yang
01:14:00
yE
Mohsin
01:14:01
yup
Aidan Higginbotham
01:14:02
Germany
Calvin Yan
01:14:02
lmao
toanphan
01:14:02
lol
Lily Yang
01:14:03
germanyy
Lily Yang
01:14:04
wooahh
Bryan Ngo
01:14:08
deutschland
felixyu
01:14:10
Wrong answers only xD
Ranelle
01:14:10
Deu
Kaan Ulupinar
01:14:10
austria
Lily Yang
01:14:11
LOLOL
Vanshaj Singhania
01:14:31
whoaa machine learning on flags??
Ranelle
01:14:40
Deutschhhhland
toanphan
01:14:41
Yep
Jake Whinnery
01:14:46
All I see is not America
Vanshaj Singhania
01:15:01
not America reax only
Ashwin Rammohan
01:15:08
1
Lily Yang
01:15:09
1
Kevin Mo
01:15:10
lool
Francisco Galvan
01:15:13
1
Lily Yang
01:15:13
wait
mengzhusun
01:15:14
1
toanphan
01:15:15
1
Franklin Huang
01:15:16
1
Ethan Wu
01:15:17
1
Francisco Galvan
01:15:20
thx Jake
ayang
01:15:25
Jake poggers
Kevin Mo
01:15:32
what is the rank of the canadian flag
Ashwin Rammohan
01:15:43
3
Vainavi Viswanath
01:15:44
2?
Bryan Ngo
01:15:44
3
ayang
01:15:45
3
Stephen
01:15:45
2?
Calvin Yan
01:15:47
2
Sal
01:15:48
3?
Ethan Wu
01:15:48
2
Vainavi Viswanath
01:15:49
3
felixyu
01:15:51
3 dawg
Ayush Sharma
01:15:52
3?
Akshay Ravoor
01:15:52
3
ayang
01:15:53
yikes blue
mengzhusun
01:15:58
3
Mohsin
01:15:58
3
Sarina Sabouri
01:16:02
3
Vanshaj Singhania
01:16:04
3
Ayush Sharma
01:16:09
White/blue/white?
Lily Yang
01:16:10
4
Zhiping Gu
01:16:10
2
Stephen
01:16:11
between the white and blue horizontal
Franklin Huang
01:16:12
3
Mohsin
01:16:12
white blue white
Ranelle
01:16:25
Dang, y’all are smart. This is like an IQ test. xD
Bryan Ngo
01:16:29
red/white/blue/white/redwhite/blue/whiteblue
Sal
01:17:05
many
mjayasur
01:17:05
3
Vainavi Viswanath
01:17:07
4
ayang
01:17:07
all i see are 50 stars yeehaw
Subham Dikhit
01:17:07
Happy st pats
felixyu
01:17:08
bre
Aidan Higginbotham
01:17:09
50
Lucas Huang
01:17:10
6
mjayasur
01:17:11
A lot
Subham Dikhit
01:17:12
Cali
Alberto Checcone
01:17:13
flag stream pog
Lily Yang
01:17:14
mUrica
Bryan Ngo
01:17:20
uncountably infinite
Alberto Checcone
01:17:32
flashbacks to 70 lecture
Vanshaj Singhania
01:17:34
wait 70 isn't for another 30 minutes
Vanshaj Singhania
01:17:48
brain too fast
Bryan Ngo
01:17:54
vexillology x linear algebra
ayang
01:18:03
thats a werid variation of the hammer sickle
Subham Dikhit
01:18:04
lol
Stephen
01:18:05
its a plow?
Lily Yang
01:18:06
knowledge is deadly
Jake Whinnery
01:18:11
Looks like a ho
Francisco Galvan
01:18:12
lol
Aidan Higginbotham
01:18:13
That's a farming ho
Jake Whinnery
01:18:14
hoe*
Hamza Kamran Khawaja
01:18:15
hoe
Calvin Yan
01:18:15
Yeah hoe
Yimo Xu
01:18:17
lol
Jake Whinnery
01:18:19
Wait how do I spell it
Franklin Huang
01:18:23
great I cannot even compress the rank of this flag
ayang
01:18:24
its hoe not ho
Ranelle
01:18:29
It’s like a Vietnam, the ussr, and a euro country put together.
toanphan
01:18:42
lol
Vanshaj Singhania
01:18:44
this chat went from ee to communism real fast
Francisco Galvan
01:18:48
that was fun, A1 chat
Jake Whinnery
01:18:51
One and the same
Stephen
01:19:07
I don't think we're in Wheeler anymore
Mohsin
01:19:17
*we don’t think we’re in Wheeler anymore
Lily Yang
01:19:31
wheeler doesn't think it's wheeler anymore
Hamza Kamran Khawaja
01:19:42
there's a wheeler?
toanphan
01:20:08
There’s a zoom link to wheeler
Franklin Huang
01:20:15
wheeler is not reachable based on our current state and control parameters, we can prove it
Hamza Kamran Khawaja
01:20:22
why is wheeler??
Ranelle
01:20:27
The life of a 3rd wheeler. :’(
Stephen
01:22:07
can someone briefly explain whats the difference between outer and inner product?
James Shi
01:22:20
outer product forms a matrix
Mohsin
01:22:27
inner product maps on to a field like the real numbers
ayang
01:22:27
inner product forms a scalar
Francisco Galvan
01:22:34
inner product synonymous with dot product?
Add
01:22:35
Outer product is column x row, inner is row x column and produces one scalar value
Francisco Galvan
01:22:37
oh, yupp
Akshay Ravoor
01:22:44
Inner product of a, b is aTb but outer product is abT
Ranelle
01:22:58
Inner product is a field? Pretty sure it’s a scalar based on the mathematical definition.
Vanshaj Singhania
01:23:09
yes its a scalar
ayang
01:23:26
"In linear algebra, an inner product space is a vector space with an additional structure called an inner product. This additional structure associates each pair of vectors in the space with a scalar quantity known as the inner product of the vectors." -wikipedia
Hamza Kamran Khawaja
01:23:29
mapping onto a field means it gets one value in the field
Stephen
01:23:33
alright, thanks
Ranelle
01:24:03
But a field in algebra (linear and abstract) is a set closed under some operations.
Jake Whinnery
01:24:12
Did someone say gram Schmidt
Ranelle
01:24:26
"In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined and behave as the corresponding operations on rational and real numbers do. A field is thus a fundamental algebraic structure which is widely used in algebra, number theory, and many other areas of mathematics.”
Franklin Huang
01:24:30
they mean as in programming: map(list) -> number
Hamza Kamran Khawaja
01:24:32
yes, like the Real numbers which you might imagine scalars from
Bryan Ngo
01:24:44
field is some abstract algebra stuff
Franklin Huang
01:24:49
yeah
Kunaal
01:24:57
usually your field is Real/Complex
Franklin Huang
01:25:01
the abstract alg def is also true
Ranelle
01:25:41
You guys should take math 113 (abstract algebra) if time permits. That’s my all-time favorite math class!
Hamza Kamran Khawaja
01:25:59
about fields?
Ranelle
01:26:14
It’s not all fields.
Ranelle
01:26:28
You learn about fields, rings, sets, cosets, etc.
Sarina Sabouri
01:26:33
Could you repeat what the purpose of the sigmas are in the equation?
Jennifer Zhou
01:27:15
^^
ayang
01:29:11
sqrt(14)
James Shi
01:29:11
sqrt14
Sal
01:29:11
14?
Vanshaj Singhania
01:29:15
sqrt(14)
gghosal
01:29:18
sqrt(14)
Kailey
01:29:25
7
Zhiping Gu
01:29:28
sqrt(1+4+9)
Ranelle
01:29:39
14^1/2
Jennifer Zhou
01:31:35
so does the sigma basically help “make” the vectors normal, or magnitude = 1
mjayasur
01:31:51
why did we insist on length 1?
vincentwaits
01:31:55
Why do we want them to be of length1?
Ayush Sharma
01:32:47
Doesn't sigma only make the u vectors orthonormal? Why would its value depend on the v vectors - wouldn't doing that make the u vectors not normalized?
Akshay Ravoor
01:33:06
Both the u and v vectors should be orthonormal
Vanshaj Singhania
01:33:12
is that kind of like with OMP where we cared about the largest contributors? (at least I think that was OMP)
mjayasur
01:33:14
So the sigma tells us how much impact each column vector in A has on the image?
Ayush Sharma
01:33:18
AH, just saw that. Thanks @Akshay!
James Shi
01:33:29
yeah OMP seems comparable
vincentwaits
01:33:40
How do we ensure that they add up to the expected dimensions of A?
Ashwin Rammohan
01:34:04
so we're expressing the matrix A as a sort of weighted combination of other rank 1 matrices?
Vanshaj Singhania
01:34:09
looking ahead, is there a threshold for sigma for us to decide whether we care about a certain element?
Ashwin Rammohan
01:34:10
and the weights are the singular values
Ashwin Rammohan
01:34:35
@vanshaj it might be like OMP where they tell u that only 2 satellites are transmitting
Ashwin Rammohan
01:34:39
so you stop at a certain pt
Vanshaj Singhania
01:34:54
interesting. thanks!
Ashwin Rammohan
01:35:11
not sure though, maybe there is a numerical bound here. we'll see!
Hamza Kamran Khawaja
01:35:20
where is the orthogonalization part? I understand the normalization part
mjayasur
01:35:24
Do we have a way from getting to a matrix a to the SVD representation?
Lily Yang
01:35:35
m x n
vincentwaits
01:35:39
N * m
toanphan
01:35:40
Nm
James Shi
01:36:30
I think we’ll get to the algorithm for SVD later
vincentwaits
01:37:30
This is awesome
Hamza Kamran Khawaja
01:37:46
why is it r(n+m)?
dylanbrater
01:37:55
yes
Ashwin Rammohan
01:38:52
@hamza in the special case where A is rank 1, there's only one outer product so just n +m. If it's higher rank, for each number up to the rank, there's another n + m.
Ashwin Rammohan
01:38:58
So r*(n + m)
Aidan Higginbotham
01:39:42
are all of those v's transposed?
Vanshaj Singhania
01:39:53
yeah
gghosal
01:39:53
Yes
Aidan Higginbotham
01:40:00
ty
Vanshaj Singhania
01:40:35
I guess that answers my threshold question lol
Jake Whinnery
01:40:37
Why is it a reasonable assumption that sigma p is much larger than sigma p+1
Aidan Higginbotham
01:40:50
Cuz we said it was @Jakes
Vanshaj Singhania
01:40:52
i think that's an assertion @Jake
Aidan Higginbotham
01:40:54
@jake*
Lily Yang
01:40:54
we ordered them
Jake Whinnery
01:41:10
I know we ordered them but that seems like a bit of a leap in logic
Subham Dikhit
01:41:18
can't see
Subham Dikhit
01:41:37
nvm, that's the toolbar
gghosal
01:41:38
I think it happens in the real world that there are a subset of sigmas that are much larger than the rest
Franklin Huang
01:41:40
it actually happens in practice, some sigmas will have 10^-large#
gghosal
01:41:42
@Jake
ayang
01:41:44
if there is a p such that op >> op+1, then we can drop the rest of the terms after op
Ayush Sharma
01:41:57
That's what I'm thinking about too @Jake - I think perhaps an equivalent solution would be to establish some kind of "threshold" sigma where all sigma less than that are discarded. But not sure about the magnitude drop part either.
Vanshaj Singhania
01:42:25
i think the idea is that you might have some decompositions that don't have a huge impact on your image, for example
Jake Whinnery
01:42:38
Oh I guess that’s why we lose image quality in compression
Vanshaj Singhania
01:42:41
so for compression purposes you leave some off
Vanshaj Singhania
01:42:41
yeah
ayang
01:42:59
This isnt a assumption that there is always a p, its a case if this p exists
Bryan Ngo
01:43:17
are there compression algorithms in use that implement SVD
Ayush Sharma
01:43:29
Also, bumping @Hamza's earlier question - during the SVD process, where do we get to assume that the rows are orthogonal to each other?
Ayush Sharma
01:43:47
Or columns?
Sal
01:43:54
So we need to compute all p values and then sort first?
Mohsin
01:44:00
@Ayush they are orthogonal by construction
mjayasur
01:44:21
so you’re image will look worse
Hamza Kamran Khawaja
01:44:24
but that means we aren't able to do this on any matrix?
mjayasur
01:44:25
When you remake it
vincentwaits
01:45:04
Thank you
Ayush Sharma
01:45:21
OK. Thanks professors and @Mohsin! :)
Hamza Kamran Khawaja
01:45:37
so to ensure, we just construct orthogonal parts?
Jake Whinnery
01:45:54
#funWithFlags
Francisco Galvan
01:45:54
side note: I feel like im actually absorbing alot more through zoom lectures since we have to slow it down more often. :) (slow learner)
Subham Dikhit
01:45:55
heck ya, flag time
Vanshaj Singhania
01:46:03
A quick shoutout to Professor Sanders for keeping up with the chat!
Bryan Ngo
01:46:05
back to vexillology x linear algebra
Alberto Checcone
01:46:15
chin stream Pog
ayang
01:46:15
we see ur chin
Hamza Kamran Khawaja
01:46:18
@Francisco, agree
Calvin Yan
01:46:18
I second Vanshaj
gghosal
01:46:18
Not yet
Alberto Checcone
01:46:30
no it's ok this is better
Francisco Galvan
01:46:30
+1 Vanshaj
mjayasur
01:46:41
Yah esp because Professor Sanders keeps up with the chat so well everyone’s questions get answered it’s lit
Ayush Sharma
01:46:42
@Hamza I believe so - I was trying to do some different examples but couldn't convince myself that the rank vectors would always be orthogonal to each other - they'd just be linearly independent. I guess they'll talk about that later, though.
Aidan Higginbotham
01:46:46
Thank you Prof Sanders for reading the chat <3
Ayush Sharma
01:46:55
+1 +1 +1
vincentwaits
01:47:04
+ 1 Thank you Professor Sanders!
Vade
01:47:23
Prof can u zoom in
Ashwin Rammohan
01:47:26
Wouldn't a rank 1 image have every column be identical?
Ashwin Rammohan
01:47:34
it seems like the columns near the stars are still different
Terrance Li
01:47:46
^ the columns can still be scaled individually
Mohsin
01:47:47
not necessarily, they can be multiples of each other
Stephen
01:47:49
zoom in please?
Ashwin Rammohan
01:47:52
oh right
dylanbrater
01:48:16
You can just full screen
mjayasur
01:48:20
Command plus
Calvin Yan
01:48:22
Press the green button on the top right
Abhik
01:48:24
cmd plus
Vanshaj Singhania
01:48:25
cmd++
Jarvis
01:48:26
command +
Ranelle
01:48:28
And command +
Aidan Higginbotham
01:48:29
cmd plus
mjayasur
01:48:29
command plus
Francisco Galvan
01:48:30
comand+ scroll in
vincentwaits
01:48:37
Ctrl and +
Ranelle
01:48:39
Two-finger scroll
Akshay Ravoor
01:48:39
Command +
Ranelle
01:48:45
Two-finger zoom*
Akshay Ravoor
01:48:47
Pinch out with fingers
Ashwin Rammohan
01:49:15
so when he says Rank 6, he means that the estimation goes up to six rank 1 matrices right?
mjayasur
01:49:17
Lol wait the norweigan flag rank 1 is still pretty good
Ashwin Rammohan
01:49:19
so 6 singular values?
ayang
01:49:34
rank 2 has different value for the intersection of the crosses
Vanshaj Singhania
01:49:49
out of curiosity, could you put this notebook on piazza after the lecture?
Vanshaj Singhania
01:49:54
oh cool
Hamza Kamran Khawaja
01:50:02
@Ashwin, yes
felixyu
01:50:12
Do professors play video games?
Hamza Kamran Khawaja
01:50:13
6 terms of the SVD equation
Hamza Kamran Khawaja
01:50:27
@felixyu +1
Lily Yang
01:50:35
wait so the flags we saw were basically an estimation of the A matrix here right
Hamza Kamran Khawaja
01:50:46
reconstruction
Bryan Ngo
01:51:08
they were SVDs of A truncated at certain ranks
Ranelle
01:51:20
Netflix and Chill -> Zoom and Relax
ayang
01:51:21
so yes, an approximation for A
Stephen
01:51:25
spotify suggestion song sucks
ayang
01:51:30
zoom and boom actually
Jake Whinnery
01:51:38
^^^
Hamza Kamran Khawaja
01:51:45
as an aside, I love this chat
mjayasur
01:51:50
me too lol
Lily Yang
01:51:52
+1
Vanshaj Singhania
01:51:53
yeah this is kinda fun
Bryan Ngo
01:51:54
zoom emotes when
Francisco Galvan
01:51:55
netflix got rid of stars ratings after Amy Schumer
Jake Whinnery
01:52:03
Oof
Vanshaj Singhania
01:52:05
makes lecture a little more community-based
Hamza Kamran Khawaja
01:52:13
heck yea
Hamza Kamran Khawaja
01:52:18
fight the corona
Ranelle
01:52:40
You guys should watch the 20-minute Pandemic episode on Netflix.
Jake Whinnery
01:52:42
This man sounds like gru
Franklin Huang
01:52:42
star trek
vincentwaits
01:52:43
Lol
Vanshaj Singhania
01:52:43
OMG
Lily Yang
01:52:45
OMG
Ashwin Rammohan
01:52:46
LOL
Hamza Kamran Khawaja
01:52:46
YES
Lily Yang
01:52:46
NO
Francisco Galvan
01:52:46
LMAO
Vanshaj Singhania
01:52:46
IM NOT THE ONLY ONE
Lily Yang
01:52:47
A;LSKDJFSAJDLF'Q
Alberto Checcone
01:52:48
YES
Vanshaj Singhania
01:52:48
YES
Stephen
01:52:48
i can't unhear it now
ayang
01:52:48
POG CHAMP
Aidan Higginbotham
01:52:48
LOL
Sal
01:52:48
dear lord
dylanbrater
01:52:49
LOL
jeremytien
01:52:49
HAHAHAHA
Bryan Ngo
01:52:49
he's on to us
Vanshaj Singhania
01:52:49
HE DOES
Lily Yang
01:52:50
SOTOTP
Hamza Kamran Khawaja
01:52:51
LMAO
Vanshaj Singhania
01:52:53
IT'S GRU
Aidan Higginbotham
01:52:53
Prof GRU
mengzhusun
01:52:55
lmao
vincentwaits
01:52:56
HAHA
dylanbrater
01:52:57
GRUUUUU
ayang
01:52:59
PROF GRU
Hamza Kamran Khawaja
01:52:59
I WAS THINKING THE SAME THING!
Ethan Wu
01:53:06
i think boser was more Gru
Ranelle
01:53:12
LMAO
Vanshaj Singhania
01:53:26
@ethan i think prof arcak sounds more like gru in person
Subham Dikhit
01:53:27
go nick!
Franklin Huang
01:53:33
LOL no idea what their tastes are
Lily Yang
01:53:55
replace final with zoom despicable me streaming
CharlieWu
01:53:58
monkaS
Ranelle
01:53:58
Boser reminded me of the German character’s voice from zombies in Black Ops.
Bryan Ngo
01:54:24
Q1: draw a circuit diagram of vector's house
Alberto Checcone
01:54:33
in order to steal the moon, we need to use svd to compress it
ayang
01:54:41
normies
Vanshaj Singhania
01:54:48
use svd to compress the gpa requirement
Ayush Sharma
01:54:50
For real though, @staff, may we have a Despicable Me-themed final question? :)
Vanshaj Singhania
01:55:54
can we extra credit for compressing despicable me
Ashwin Rammohan
01:56:30
so sigma here would be how important brightness is compared to other factors?
Akshay Ravoor
01:56:50
correct
Franklin Huang
01:56:54
*a disentanglement problem is slowly bubbling up to the surface*
vincentwaits
01:57:45
YES PLS
felixyu
01:57:48
😣
Vanshaj Singhania
01:57:49
yesss
Hamza Kamran Khawaja
01:57:53
but for separate movies, brightness for comedy, for action etc
Ashwin Rammohan
01:57:53
this is rly cool
Ranelle
01:57:57
Alright, see you Thursday, class! It was fun learning and chatting!
Francisco Galvan
01:57:57
THANK YOU PROFESSORS, the both of you guys!
Stephen
01:57:59
thank u
vincentwaits
01:58:00
THANK YOU!!!
Jennifer Zhou
01:58:02
Thank you!!!!
Ashwin Rammohan
01:58:02
thank you
Vanshaj Singhania
01:58:02
thank you guyssss
Lily Yang
01:58:03
THANKS
Ayush Sharma
01:58:03
Thank you! :D
Franklin Huang
01:58:04
thank you!
Hamza Kamran Khawaja
01:58:04
GREAT LECTUER
Jamie
01:58:06
thank you!
dylanbrater
01:58:06
thx
Hamza Kamran Khawaja
01:58:06
LECTURE
Abhik
01:58:08
thank you!
Sal
01:58:09
this was such a good lecture
ayang
01:58:12
ty prof gru
Ranelle
01:58:12
Muchas gracias, profesores!
Calvin Yan
01:58:14
No questions. Thanks so much!
Evelyn Pinjen Wu
01:58:15
Thank you!
Kunaal
01:58:15
thank you
felixyu
01:58:16
Do profs play video games? @seth
Jiachen Yuan
01:58:18
thx
Helen Peng
01:58:20
thank you!!!!!
Alberto Checcone
01:58:22
can we have more chin streams
Sarina Sabouri
01:58:23
Thank you so much!
Aidan Higginbotham
01:58:23
Thank you!
vincentwaits
01:58:23
You too Professor Seth!
Vanshaj Singhania
01:58:25
can we add you guys on discord
Francisco Galvan
01:58:26
100/1000+ students curr attending
Jiachen Yuan
01:58:26
really cool examples!
Add
01:58:29
Thanks!
Jenny
01:58:31
discorddd
Francisco Galvan
01:58:31
lol
vincentwaits
01:58:32
:)
Lily Yang
01:58:32
:))))))
Jessie Siyi Ren
01:58:33
Very clear, thanks!