Hello people, welcome back to my channel.
The topic for today's video is how does a
neural network learn? So in the last video?
We saw what was weights and biases and how
does it interfere in between computation of
neural network? So in today's video Let's
Take a data set and we see how does a particular
neural network Builder. So essentially we
have a data set that is for the monthly expense.
So say this So the monthly expense for a particular
student. So like different factors will contribute
to the monthly expense. So say if I am a student
that is a foreign student. I need to give
Insurance then rent then for food then for
miscellaneous and then say like you have a
final class that is a target attribute as
age greater than or 30 or not. So this is
the class that we are going to predict now
these Figures are random. This is not the
real one that mine or either of my friends
give so this is just a random number that
I would so we have different figures that
is represented in Euros. So whenever you input
into the system that is a neural network,
you will input into some real value numbers
so you won't have this string. So now say
this is for one particular student. This is
for second student third student and you have
many such records so
I have taken 100 and 50 K. It is 1,50,000
records for this now save like if you ask
like whether do we require really a neural
network for computing this kind of activity
will I say it's no but if you are just three
or four persons, then that would be like simple
for you to calculate in your simple calculator
or scientific or other calculators. But if
you have a large number of data sets shape
a particular pool of Our category of students
of one particular department and if you are
asked to take a survey of their monthly expenditure,
how does it impact the economy or how much
tags they would be paying in certain situations.
Then you need to have to build a certain kind
of particular neural and Morgan ordered in
this ticket that particular activity. So if
you are data set is very small, then you don't
require neural network else if you have a
larger number of data set like In particular
survey or the current situation say there
are different records or different data sets
of persons suffering from this COVID disease.
So in that case, if you want to do a particular
survey, it becomes difficult where you compute
certain activities by hand. So they're in
that case a neural network comes into the
picture. So what we do is I just label this
as my input. So I have x 1 x 2 x 3 x 4 and
this is the class. So this is essentially
a binary attribute that is yes or no, so I
would be using a sigmoid function. So essentially
what I have is I have X1 X2 X3 and X4. These
are my four inputs Insurance, rent, food and
miscellaneous. You can consider any appearances
or any other items that you would be by Lying
beside so apart from all these three attributes.
So all these attributes are independent. So
that is the most important thing. We need
to consider. So for any algorithm, it assumes
that all attributes are independent. Neither
of them has any dependencies among them. So
what I have is say, for instance, I have my
first hidden layer. So this is my input layer.
This is my hidden layer.
And in that, I have to 3 units and the Hidden
layer, I have two units and in the final output
layer. I have one unit. So let me label this.
This is my input layer. This is my first hidden
layer. This is my second hidden layer. And
this is my output layer. I have these connections
going so this gets inputs this way.
And then you have the final output that is
by predicted. So we know that this it has
two parts that this linear part and nonlinear
part. So I'm using the non-linear function
as a sigmoid function. So, you know the weight
Matrix is how it is computed and the number
of coefficients including the biases, which
it has got so say I am feeding this particular
inputs. Each of them one by one so say for
one particular double. So this is a say like
this is an Excel file dot XLS our ME.XLS file
and you are uploading this to your neural
into so this takes inputs from The Real World
so there Group particular sensor where it
would be feeding at each and every day say
for instance. If you are taking this particular
record, so record whenever I say there is
a horizontal cut that is one particular instance
all are real-valued numbers going to this.
It does some computation and it will go through
the second hidden layer and then it will finally
output say at the output you put a threshold
say you have This sigmoid function say it
is like this. So here's an I'm putting a threshold
less than point five. Whatever the threshold
value is less than 0.5 that belongs to NO
class and greater than 0.5 that belongs to
the YES class. So if the particular setup
so initially the weights That I assign are
random. So why the weights are random that
we'll discuss in some other video for the
time being just consider the weights that
we assigned to learn this is random. So essentially
when we build a neural network, our major
goal is to lay learn these weights. So learning
these weights and biases are the major goal
that we achieved by building a neural network.
So say in the first particular output what
we get is less than 0.5. That is the threshold
after this computation. And we checked with
our data set so it's correct. So we need not
adjust the weight for that particular iteration
now for the second instance or for the second
record if we get the output as again less
than 0.5 but here in our data set in certain
SES. So this is obtained by the real-valued
record. So this need not be wrong, but our
neural network can be wrong. So this updation
is mainly done at the weights. So initially
you have updated the weights randomly and
also the biases so we need to learn those
weights and we need to adjust that. So that
is our major goal this and subsequently for
all of these different records, which are
there in this data set. You basically just
go back at each different layer. So this backtracking
from one particular layer to be the previous
layer is done by an algorithm called Backpropagation
And the weight of decision at each particular
unit is done with the help of gradient descent.
So gradient descent, we have already learned
in our linear regression. So here in this
case. Our cost function would be between the
weights and the biases so these two are the
coefficients is dependent on our cost function.
So we go at each level one by one and at each
unit. We try to generalize this particular
unit. So this can be assumed light say I have
this particular wireframe structure. This
is a three-unit. So X1 X2 X3 and then at the
first hidden layer, you have two units and
then at the second you have two units and
then you have an output so you get the outputs
for this and this you feed to the next layer
and it propagates and so for at the final
you get this probability value and if it does
not match with the weights, which you have
assigned randomly that this particular setup
that is this weight Matrix, you will Dig this
so essentially if there is something problematic
with the bias. So basically you add just the
since I cannot move this so this will be like
initially disliked this or if there is a change
in the pious at this particular unit. It will
just shift this towards up or it will shift
this torsion. So assume that this is a joint
framework so you can just move this. So this
is how this particular neural network will
Work. So essentially I don't this particular
input Vector. This will be a column Vector.
So you have x 1 x 2 x 3 and x 4 For multiplication
you need say n cross 1 Matrix. So then you
multiply with the weight. So that is how matrix
multiplication works. And one more thing is
that when we just compute this that is you
have this W A plus b that is the output so
we have generalized this equation in this
So essentially when we do the matrix multiplication
this bait Matrix is taken as the transpose
and not the original weight Matrix. So that
is the change that we see here and also the
loss function Would be written as J with respect
to W and B is given by 1 upon n summation.
I running from 1 to n loss of Y and Y cap.
That is won't you get and what do you want?
So if there is a change in between those two,
you will just update the weight accordingly.
So well that is how a particular neural network
will learn the weights at each particular
instance. So that was all regarding learning
a particular neural networking deep learning.
So hope you guys enjoy this video if you found
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