12a: Neural Nets Video Lecture | Artificial Intelligence: A Fundamental Guide - AI & ML

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it was in 2010 yes that's right it was
in 2010 we were having our annual
discussion about what we would dump from
603 for in order to make room for some
other stuff and we almost killed off
neural nets that might seem strange
because you know our heads are stuffed
with neurons if you open up your skull
and pluck them all out you don't think
anymore so it would seem that neural
nets would be a fundamental and
unassailable topic but many of us felt
that the neural models of the day
weren't much in a way of faithful models
of what actually goes in inside our
heads and besides that nobody had ever
made a neural that was worth darn for
doing anything so we almost killed it
off but then we said well the everybody
feel cheated if they take a course in
artificial intelligence don't learn
anything about neural nets and then I go
off and invented themselves and all
waste all sorts of time so we kept the
subject in then two years later geoff
hinton from the university of toronto
stunned the world with some real net
work he had done on recognizing and
classifying pictures and he published a
paper from which I am now going to show
you a couple of examples
Geoff's a neural net by the way had 60
million parameters in it and it was his
purpose was to determine which of a
thousand categories best characterized a
picture
so there it is there's a sample of
things that the Toronto neural net was
able to recognize or make mistakes on
I'm going to blow that up a little bit I
think I'm going to look particularly at
the example there later labeled
container ship so what you see here is
that the program returned its best
estimate of what it was ranked verse 5
according to the likelihood of
probability or the certainty that it
felt that a particular class was
characteristic of the picture and so you
can see this one is extremely confident
that it's a container ship it also was
fairly moved by the idea that might be a
lifeboat now I'm not sure about you but
I don't think this looks much like the
lifeboat but it does look like a
container ship so if I look at only the
best choice it looks pretty good here
are the other things that it did pretty
well on got the right answer is the
first choice is this first choice so
over on the left you see that it's
decided that the picture is a picture of
a might the might is not anywhere near
the center of the picture but somehow it
managed to find it the container ship
again there's a motor scooter a couple
people sitting on it but it correctly
characterized the picture as a motor
scooter and then on the right a leopard
and everything else is a cat of some
sort so it seems to be doing pretty well
in fact it does do pretty well but
anyone who does this kind of work has an
obligation to show you some of the stuff
it doesn't work so well on or it doesn't
get quite right and so these pictures
also occurred in Hinton's paper so the
first one is characterized as a grill
but the right answer was supposed to be
convertible oh no yes yeah right answer
was convertible the second case the
characterization is of a mushroom and
the alleged right answer is a garret is
that pronounced right it turns out
that's a kind of mushroom so no problem
there in the next case it said it was a
cherry
but it was supposed to be a Dalmatian
now I think a Dalmatian is a perfectly
legitimate answer for that particular
picture so hard to faulted for that in
the last case it didn't you know the the
correct answer was not in any of the top
five I'm not sure if you've ever seen a
Madagascar cat but that's a picture of
one and it's interesting to compare that
with the first choice of the program
squirrel monkey this is the two
side-by-side so in a way it's not
surprising that it thought that the
madagascar cat was a picture of a
squirrel monkey so pretty impressive it
blew away the competition it did so much
better the second place wasn't even
close
and for the first time demonstrated that
a neural net could actually do something
and since that time in the three years
since that time there's been an enormous
amount of effort put into neural net
technology which some say is the answer
so we're going to do today and that
tomorrow is have a look at this stuff
and ask ourselves why it works when it
might not work what needs to be done
what has been done and all those kinds
of questions will emerge so I guess the
first thing to do is to think about what
it is that we are being inspired by
we're being inspired by those things
that are going that are inside our head
all 10 to the 11th of them and so if we
take one of those 10 to the 11th and
look at it you know from 7 of something
or rather approximately what a neuron
looks like and by the way I'm going to
teach you in this lecture how to answer
questions about neurobiology with an 80%
probability that you will give the same
answer as a neurobiologist ok so let's
go so here's a neuron it's got a cell
body in there is a nucleus and then out
here is a long thingamajigger which
divides maybe a little bit but not much
and we call that the axon
so then over here we've got this much
more branching type of structure that
looks maybe a little bit like so I know
maybe like that and this stuff branch is
a whole lot and that part is called a
dendritic tree now there are a couple of
things we can note about this is that
these guys are connected of axon to
dendrite so over here there will be a
so-called presynaptic thickening and
over here will be some other neurons
dendrite and likewise over here some
other neurons axon is coming in here and
hitting the dendrite of our set of the
one occupies most of our picture so if
there's enough stimulation from this
side in the axonal tree or the dendritic
tree then a spike will go down that axon
it acts like a transmission line and
then after that happens the after that
the neuron will go quiet for a while is
it's kind of recovering it's straight
that's called a refractory period now if
we look at them that connection in a
little more detail this little piece
right here it sort of looks like this
here's the axon coming in it's got a
whole bunch of little vesicles in it and
then there's a dendrite over here and
with us and when the axon is stimulated
it dumps all these vesicles into this
inner synaptic space for a long time it
wasn't knowing whether those things were
actually separated I think it was Mamoni
Hall who demonstrated that the that one
neuron is actually not part of the next
one there actually
operated by these synaptic by the
synaptic gaps so oh there it is how can
we model that sort of thing well here's
what's usually done here's what is done
in the neural net literature first of
all we've got some kind of binary input
because these things either fire or they
don't fire
so it's an all-or-none kind of situation
so over here we have some kind of input
value we'll call it x1 and it's either a
0 or a 1 so it comes in here and then it
gets multiplied times some kind of
weight we'll call it w1 so this this
part here is sort of modeling this
synaptic connection it may be more or
less strong and if it's more strong this
weight goes up and if it's less strong
this weight goes down so that's that's
the that's the influence that reflects
the influence of the synapse on whether
or not the whole axon besides is
stimulated and we got other inputs down
here X a man also zero Y it's also
multiplied by a weight we'll call that W
Seban and now we have to somehow
represent the way in which these these
inputs are collected together how that
how they have collective force and we're
going to model that very very simply
just by saying ok go right on through a
summer like so but then we have to
decide if the collective influence of
all those inputs is sufficient to make
the neuron fire so we're going to do
that by running this guy through a
threshold box like so here is what the
box looks like in terms of the
relationship between input and the
output and what you can see here is that
nothing happens until the input exceeds
some threshold T
if that happens then the output Z is a 1
otherwise it's a zero so binary and
binary out we model the synaptic weights
by these multipliers we model the
accumulating accumulative effect of all
that's input to the neuron by a summer
we decide if it's going to be an
all-or-none 1 by running it through this
threshold box and seeing if the subway
some products add up to more than
threshold if so we get a 1 so what in
the end are we in fact modeling well
with this model
we have number one all or not number two
cumulative influence and number three oh
I suppose synaptic weight but that's not
there might be to model in a real neuron
we might want to deal with the
refractory period not note in these
biological models that we build neural
nets out of we might want to model
axonal bifurcation we do get some
division in the axon of the neuron and
it turns out that that pulse will either
go down one branch or the other and
which branch it goes down depends on
electrical activity of the vicinity of
the division so these things might
actually be fantastic coincidence
detectors but we're not modeling we
don't know how it works so excellent oh
by FERC ation might be ma we might also
have a look at time patterns see what we
don't know is we don't know if the
timing of the arrival of these pulses in
the dendritic tree has anything to do
with what that neuron is going to
recognize all right so a lot of unknowns
here and now I'm going to show you how
to answer a question about neurobiology
with 80% probability you'll get it right
just say we don't know
and that will be with 80% probability
what the neurobiologists would say so
this is a model inspired by what goes on
in our heads but it's far from a it's
far from clear if what we're modeling is
the essence of why those guys make
possible what we can do
nevertheless so we're going to start
we're going to go so we've got this
model of what it's what a neuron does so
what about what is a collection of these
neurons do well we can think of your
skull as a big box full of neurons maybe
a better way to think of this is that
your head is full of neurons and they in
turn are full of weights and thresholds
like so so into this box come a variety
of inputs X 1 through X m and these find
our way to the inside of this gaggle of
neurons and out here become a bunch of
outputs c1 to Z N and a whole bunch of
these maybe like so there are a lot of
inputs like so and somehow these inputs
through the influence of the weights and
the thresholds come out as a set of
outputs so we can we can write that down
a little fancier by just saying that Z
is a vector which is a function of well
certainly the input vector but also the
weight vector and a threshold vector so
that's all in there on that is and when
we train a neural net all we're going to
be able to do is adjust those weights
and thresholds so that what we get out
is what we want so a neural net is a
function approximator it's good to think
about that it's a function approximator
so maybe we've got some sample data that
gives us an output vector that's desired
as a function as another function of the
input forgetting about what the weights
and the thresholds are that's what we
want to get out and so how well we're
doing can be figured out by comparing
the desired value with the actual value
so we might think then that we can get a
handle on how well we're doing by
constructing some performance function
which is determined by the desired
vector and the input vector sorry the
desired vector and the actual output
vector for some particular input or for
some set of inputs and the question is
what what should that function be how
should we measure performance our given
that we have what we want out here and
what we actually got out of here
well one simple thing to do is just to
measure the magnitude of the difference
that makes sense but of course that
would give us a performance function
that is a function of the distance
between those vectors would look like
this but that turns out to be
mathematically inconvenient in the end
so how do you think we're going to turn
it up a little bit what's that well I
don't know how about this we squared
that way we're going to go from this
little sharp point down there to
something looks more like that so it's
best when the difference is zero of
course and it gets worse as you move
away from zero but what we're trying to
do here is we're trying to get to a
minimum value and and I hope you'll
forgive me I just don't like the
direction we're going here because I
like to think in terms of improvement is
going uphill instead of downhill
so I'm going to dress this up just one
more step put a minus sign out there and
then our performance function looks like
this it's always negative and it gets
and the best value it could possibly be
a zero so that's where we're going to
use just because I am Who I am it
doesn't matter right still you're trying
to either minimize or maximize some
performance function okay so what are we
going to do I guess what we could do is
we could treat this thing what we
already know what to do
I'm not even sure we're ever devoting a
lecture to this because it's clear that
what we're trying to do is we're trying
to take our weights and our thresholds
and adjust them so as to maximize
performance so we can make a little
contour map here with a simple neural
net with just two weights in it and
maybe it looks like this contour map and
at any given time we've got a particular
W one in particular - W - and we're
trying to find a better W 1 and W 2 so
here we are right now and there's the
contour map and this is 603 4 so what do
we do a simple simple matter of
hill-climbing right so we'll take a step
in every direction if we take a step in
that direction not so hot that actually
goes pretty bad these two are really
ugly ah but that one that one takes up a
hill a little bit so we're done except
that I just mentioned it Hinton's neural
net has 60 million parameters in it so
we're not going to hill climb with 60
million parameters because that's high
it explodes exponentially in the number
of weights you've got to deal with the
number of steps you can take so so this
perch is computationally intractable
fortunately you've all taken 801 1801 or
the equivalent thereof so you have a
better idea instead of just taking a
step in every direction what we're going
to do is we're going to take some
partial derivatives
and we're going to see what does they
suggest to us in terms of how we're
going to get get around in his space so
we might have the parcel of that
performance function up there with
respect to w1 and we might also take the
partial derivative of that guy with
respect to w2 and these will tell us how
much improvement we're getting by making
a little movement in those directions
right there how much how much a changes
give them that we're just going right
along the axis so maybe what we ought to
do is if this guy is much bigger than
this guy it would suggest that we mostly
want to move in this direction or to put
in 1801 terms what we're going to do is
we're going to follow the gradient and
so the change and the W vector is going
to equal to this partial derivative
times I plus this partial derivative
times J so what we're going to end up
doing in this particular case by
following that formula is moving off in
that direction right up right up the
steepest part of the hill and you know
how much we move is a question so let's
just have a rate constant R that decides
how big our step is going to be and now
you think we were done well too bad for
our side we're not done there's a reason
why we can't use gradient ascent or in
the case that I've drawn our gradient
descent if we take the performance
function the other way why can't we use
it
the remark is local maxima and that is
certainly true but it's not our first
obstacle
when does gradient descent work ah
there's something wrong with our
function that's right it's nonlinear or
it's rather it's discontinuous so
gradient centric wires a continuous
space continues surface so too bad for
our side it isn't so what to do well
nobody knew what to do for 25 years
people were screwing around with
training neural nets for 25 years before
Paul where Bo's sadly at Harvard in 1974
gave us the answer now I want to tell
you what the answer is first part of the
answer is those thresholds are annoying
it just suggests extra baggage to deal
with what we really like instead of Z
being a function of X W and T was a like
C prime to be a function f prime of X
and the weights but we got to account
for the threshold somehow so here's how
you do that
what you do is you say let us add
another input to this neuron and it's
going to have a weight W 0 all right and
it's going to be connected to an input
that's always minus 1 be with me so far
we're just going to add enough now what
we're going to do is we're going to say
let W 0 equal T what's that do the two
movement of the threshold
what it does is it takes that threshold
moves it back to zero so this is a
little trick here takes this pink
threshold and redoes it so that the app
that the new threshold box looks like
this all right think about it if if this
is T and this is minus 1 this is minus T
and so this thing ought to fire if
everything is over if the subs over zero
so it makes sense it gets greater the it
gets rid of the threshold thing for so
now we can just think about weights but
still we've got that we've got that
we've got that step function there and
that's not good so what we're going to
do is we're going to smooth that guy out
so this is trick number two instead of a
step function we're going to have this
thing we lovingly call a sigmoid
function because it's kind of going to S
type shape and the function we're going
to use is just one one what better make
it a little bit different 1 over 1 plus
e to the minus whatever the input is
let's call the input alpha that makes
sense to see if alpha is 0 that's 1 over
1 plus 1 so it's 1/2 if alpha is
extremely big then e to the minus alpha
is extremely small it becomes 1 goes up
to an asymptotic value 1 here on the
other hand if alpha is extremely
negative then e to the minus alpha is
extremely positive and it goes to 0
asymptotically so we got the right look
to that function it's a very convenient
function did God say that neurons ought
to be that threshold lot of work like
that
no God didn't say so who said so the
math says so it has the right shape and
look and the math and it turns out to
have the right map well as we'll see in
a moment ok
so let's see where are we we decided
that what we'd like to do is take these
partial derivatives we noted there was
awkward to have those thresholds so we
got rid of them and we noted it was
impossible to have the step function so
we got rid of it now we're in a
situation where we can actually take
those partial derivatives and see if it
gives us a way of training the neural
net so as to bring the actual output
into alignment what we desire alright so
the deal with that we're going to have
to work with the world's simplest neural
net now if we've got one neuron it's not
a net but if we've got two word neurons
we've got a net it turns out that's the
world's simplest neuron so we're going
to look at it not 60 million parameters
but just a few actually just two
parameters so let's draw it out we've
got an input X that goes into a
multiplier and it gets multiplied times
w1 and that goes into a sigmoid box like
so we'll call this p1 by the way product
number one out here comes why why gets
multiplied times another weight we'll
call that w-2 then that produces another
product which we'll call p2 and that
goes into a sigmoid box and then that
comes out as Z and Z is the number that
we use to determine how well we're doing
and our performance is actually going to
be our performance function P is going
to be one half minus one half because I
like things are going in that direction
times the difference between the desired
output and the actual output squared
okay so now let's decide what those
partial derivatives are going to be
oh let me do it over here
so what are we trying to compute partial
of the performance function P with
respect to w2
okay well let's see
you know partial we're trying to figure
out how much this wiggles when we wiggle
that alright but you know it goes
through this variable p2 and so maybe
what we can do is figure out how much
this wiggles when we go how much z
wiggles when we wiggle p2 and then how
much p2 wiggles only wiggle w2 and just
multiply those together I forget what's
that call an eighty know something or
other this chain rule so we're going to
do is we're going to rewrite that
partial derivative using chain rule and
all it's doing is saying that there's an
intermediate variable and we can compute
how much that end wiggles with respect
to how much dead end wiggles by
multiplying the how much of the other
guy's wiggle let me write it down at
baek's where it sets in mathematics so
that's going to be equal to the partial
of P with respect to Z times the partial
of P oh sorry partial of Z with respect
to p2 Wow keep me on track here partial
of Z with respect to w2 now I'm going to
do something for which I will hate
myself I'm going to erase something on
the board I don't like to do that but
but you know what I'm going to do done
I'm going to say this this this is true
by a chain rule but look I could take
this guy here and screw around with it
with a chain rule too and in fact what
I'm going to do is I'm going to replace
that with partial of Z with respect to
p2 and partial of p2 with respect to w2
right so I didn't erase it after all but
you can see what I'm going to do next
I'm going to do the same thing with the
other partial derivative but this time
instead of writing down and writing over
I'm just going to expand it all out one
go I think so partial of P respect to w1
is equal to the partial of P with
respect to Z the partial of Z with
respect to P to the partial of p2 with
respect to what Y partial of Y with
respect to p1 partial of P 1 with
respect to w1 so that's just kind of
going like a zipper down that's that
string of variables expanding each bar
using chain rule until we got to the end
so there are some expressions that
provide those partial derivatives but
now if you don't if you'll forgive me it
was convenient to write them out that
way except that match the intuition in
my head but I'm just going to turn them
around about just some it's just a
product I'm just going to turn them
around so partial P to partial W 2 times
partial of Z partial P 2 times the
partial of P with respect to Z same
thing and now this one keep me on track
because if there's a mutation here will
be fatal
partial of P 1 partial W 1 partial of Y
partial P 1 partial P 2 partial of Y
parcel of Z times the partial of p2
partial of performance function respect
to Z okay now all I have to do is figure
it out figure out what those parcels are
and we have solved this little simple
neural net so that's going to be easy
oh where are where is my board space
let's say parcel of p2 we expect it to
what partial that's that's the product
parcel of Z partial the performance
function with respect to Z oh now I can
see why I wrote it down this way let's
see it's going to just be D minus Z we
can do that one in our hand what about
the partial of p2 is expect to w2 well
p2 is equal to Y times w2 so that's easy
that's just Y now all we have to do is
figure out the partial of Z with respect
to p2 crapple is just it's going through
this this threshold box so I don't know
exactly what that partial derivative is
so we'll have to figure that out right
because the function relating them is
this is this guy here and so we have to
figure out the partial of that with
respect to alpha all right so we got to
do it there's no foot there's no way
around it so we have to destroy
something ok we're going to destroy our
neuron
so the function we're dealing with is
we'll call it beta is equal to 1 over 1
plus e to minus alpha and we want what
we want is the derivative with respect
to alpha beta and that's equal to D by D
alpha of you know I can never remember
those quotient formulas so I'm going to
rewrite it a little different way I want
to write it as 1 minus e to the minus
alpha to the minus 1 so I because I
can't remember the formula for
differentiated quotient
okay so let's differentiate it so that's
equal to 1 minus alpha 1 minus e to the
minus alpha to the minus 2 and we got
that minus comes out of that part of it
then we get differentiate D it's got
differentiate the inside of that
expression and when we differentiate it
the inside of that expression we get e
to the minus alpha yeah oh yeah sorry
thank you that was that was one of those
fatal mistakes you just prevented that's
one plus is this one plus here too okay
so so we've differentiated that we've
turned that into a minus 2
we brought the minus sign outside then
we're differentiating the inside the
derivative of an exponential is an
exponential then we go differentiate
that guy and that just helps us get rid
of the minus sign reduced so that's the
derivative I'm not sure how much that
helps
except that I'm going to perform a
parlor trick here and rewrite that
expression Leslie I'm going to say
that's going to be e to the minus alpha
over 1 plus
e to the minus alpha times 1 over 1 plus
e to the minus alpha naught okay I've
got a lot of nodding heads here so I
think I'm saying unsafe ground but now
when you perform another polar trick I'm
going to add 1 which means I also have
to subtract 1 all right that's
legitimate isn't it so now I can rewrite
this as 1 plus e to the minus alpha over
1 plus e to the minus alpha minus 1 over
1 plus e to the minus alpha times 1 over
1 plus e to the minus alpha hey high
school kid could do that I think I'm on
safe ground oh wait this is beta this is
beta oh sorry wrong wrong side better
make this beta and this one any high
school kid could do it ok so what we've
got then is that this is equal to 1
minus beta times beta that's the
derivative and that's weird because the
derivative of the output with respect to
the input is given exclusively in terms
of the output strange doesn't really
matter but it's a curiosity and what we
get out of this is that that partial
derivative there that's equal to well
the output is p2 no the output is Z so
it's C times 1 minus C so whenever we
see the output the derivative one of
these sigmoids with respect to its input
we can just write the output type 1
minus output we've got it
so that's why it's mathematically
convenient it's mathematically
convenient because when we do this
differentiation
we get a very simple expression in terms
of the output we get a very simple
expression that's all we really need so
would you like to see a demonstration
this is demonstration of world's
smallest neural net in action where as
no that's here we go so there's our
neural net what we're going to do is
we're going to Train it to do absolutely
nothing we're going to do is train it to
help make the output the same as the
input now what I call a fantastic leap
of intelligence but let's see what
happens well nothing's happening
well it finally got to this point where
the maximum error not the performance
but the maximum error went below a
threshold that I had previously
determined so if you look at the input
here and compare that with the desired
output on the far right you see it
produces an output which compared with
the desired output it's pretty close so
we can test the other way like so and we
can see that the desired output is
pretty close to the actual output in
that case - and it took 694 iterations
to get that done let's try it again
Oh to 823 of course this is all
consequences just starting off with
random weights by the way if you started
off with the weights all the weights
being the same what would happen nothing
because they'd always stay the same so
you've got to put some randomization
into the beginning so took a long time
maybe the problem is our rate constants
too small so let's try it let's crank up
the rate constant or more old it and see
what happens
nah that was pretty fast let's see if it
was a consequence of random chance run
now it's pretty fast they are 57 times
57 iterations third try 67 so it looks
like my initial rate constant was too
small so if point 5 was not as good as
5.0 why don't we try and get up to 50
and see what happens Oh in this case 124
let's try it again ah
in this case 117 so it's actually gotten
worse and not only has it gotten worse
you'll see that there's a little bit of
a little bit of instability showing up
as of course is along its way toward a
solution so what it looks like is that
if you've got to wait rate constants to
small it takes forever
you got a rate constants too big it can
sort of jump too far as in my diagram
which is somewhere underneath the board
you can you can go all the way across
the hill and get to the other side so
you have to be careful about the rate
constant so what you really want to do
is you want your rate constant to vary
with what's happening in the as you
progress toward an optimal part an
optimal performance so if you if you if
your performance is going down when you
make the jump you know you've got a rate
constants too big if your performance is
going up when you make a jump maybe you
want to increase it bump it up a little
bit until it until it's a little it
doesn't look so good okay so is that all
there is to it well not quite because
this is the world's simplest neural net
and maybe we have to look at the world's
second simplest neural net
now let's call this well let's call this
X what we're going to do is going to
have a second input and I'm just I don't
know maybe this is screwy I'm just going
to use color coding here to
differentiate between the two inputs and
and a and the stuff they go through
maybe I'll call this Z 2 and this C 1
and this X 1 and X 2 now if I do that if
I've got two inputs and two outputs then
then my performance function is going to
have two numbers in it
the two desired values and the two
actual values and I'm going to have two
inputs but you know it's the same stuff
I just repeat that I just repeat what I
did in white only I make it Orange
oh but what happens if what happens in
Phi I do this
say pretty little cross connections in
there so these these two streams are
going to interact and then there might
be some you know this this Y can go into
another multiplier here and go into a
summer here and likewise this Y can go
up here and into a multiplier like so
and there are weights all over the place
like so this guy goes up into here and
now what happens now we've got a
disaster on our hands because there are
all kinds of paths through this network
and you can imagine that if this was not
just two neurons deep but three neurons
deep what I would find is expressions
that look like that but you know you
could go this way and then down through
here and out here or you could go this
way and then back up through here so it
looks like there is an exponentially
growing number of paths through that
Network and so we're back to an
exponential blow-up and it won't work
yeah won't work except that we need to
let the mass sing to us a little bit and
we need to look at the picture and the
reason I turn this guy around was
actually because from a point of view of
letting the math sing to us this piece
here is the same as this piece here so
part of what we needed to do to
calculate the partial derivative with
respect to w1 has already been done when
we calculated the partial derivative
with respect to w2 and not only that if
we calculated the partial with respect
to these breen w's at both levels but
would discover is that that sort of
repetition occurs over and over again
and now I'm going to try to give you an
intuition 'el idea of what's going on
here rather than just right down to math
and salute it and and here's here's a
way to think about it for an intuitional
point of view whatever happens to this
performance function that's back of this
row back back back of these Peas here
the stuff over there can influence P
only by going through influenced
performance only going through this
column of piece and there's a fixed
number of those so it depends on the
width not the depth of the network so
the influence of that stuff back there
on P is going to end up going through
these guys and it's going to end up
being so that we're going to discover
that a lot of what we need to compute in
one column has already been computed in
a column on the right so it isn't gonna
explode exponentially because the
influence let me say it one more time
the influence of changes the influences
of changes in P on the performance
there's all we care about when we come
back to this part of the network
because this stuff cannot influence the
performance except by going through this
column of peace so it's not going to
blow up exponentially we're going to be
able to reuse a lot of the computation
so it's the reuse principle have you
ever seen a reuse principle at work
before not exactly but you remember that
little business about the extended list
we know that we've seen help we know
we've seen something before so we didn't
stop computing it's like that we're
going to be able to reuse the
computation that so we've already done
so to prevent an exponential blow-up by
the way for those of you know about fast
Fourier transform
same kind of idea reuse of partial
results so in the end what can we say
about this stuff in the end what we can
say is that it's linear and depth now
does it say if we increase the number of
layers the so called depth then we're
going to increase the amount of
computation necessary in a linear way
because the computation we need in any
column is going to be fixed what about
how it goes with respect to with respect
to the width
well we respect to the width anything on
here can be connected any year on the
next row so the amount of work we're
going to have to do will be proportional
to the number of connections so respect
to width it's going to be W squared but
the fact is that in the end this stuff
is readily computed and this
phenomenally enough was overlooked for
25 years so what did it take what what
is it in the end in the end it's
extremely simple idea all great ideas
are simple how come there are more of
them well because frequently that
simplicity involves finding a couple
tricks and making a couple of
observations so usually we we humans
hardly ever go beyond one
Trick or one observation but if you
cascade a few together sometimes
something miraculous falls out that
looks in retrospect extremely simple so
that's why we got the reuse principle at
work trying to reuse computation in this
case the miracle was consequence of two
tricks plus an observation and the
overall idea is all great ideas are
simple and easy to overlook for a
quarter century