Let's start with but Sprunt, like in biology, a single cell of our nervous system is called a neuron
in artificial neural networks.
One of the earliest such artificial neuron was a Perceptron.
Perceptron was delivered in 1950s.
Yes, the work on your networks began nearly 70 years ago.
Today, we use other models of artificial neurons, such as sigmoid neurons, to understanding my neurons.
We need to first look at de Perceptron.
Here's a simple pictorial representation of how Perceptron works.
Perceptron is this circle or a black box, which takes in several binary inputs x1, x2, x3 and so
on the exam and produces a single binary output represented by light, by binary input and binary output.
I mean that these variables can only take two values, for example, zero and one true or false, etc..
There are several ways in which these x1 x2 x3 can give us the desired output way.
One of these rule is
that we will multiply each of these input values with weights, W1, W2, WTT and then compare.
If the final value of the sum of these products is greater than a threshold value or nought, if the
sum value is greater, then the Perceptron gives an output value of one.
And if it is less than special, it gives out an output value of zero.
Mathematically, this is how we represent this logic.
This is the summation of weights with feature values.
Basically, this means explain it to W one plus X2 and W2 plus X3 into WTT and so on the exam into W.M..
The sum of all these product.
Is this left random?
We compared this some, we did threshold value.
If this is less than the threshold, we give an output of zero.
If it is more than the threshold, we give our product one.
Let's take a simple example, which may not be very realistic, but you will get the idea of how this
Perceptron functions.
Let's say you want to decide whether you should put is a particular shirt or not.
You might make your decision by weighing up three factors, whether this shirt is blue or not, whether
the shirt is falsely or half sleeve and whether the fabric is cotton or not.
We can represent these three variables using three binary variables.
For instance, X1 is equal to one.
If the shirt is blue and it is zero, if it is not blue, x2 is equal to one.
If it is full lead and zero if it is half sleeve and x3 is equal to one for cotton fabric and zero for
non-current fabric.
Now, suppose that you absolutely adored Blue-Collar chert and you would prefer full sleeved cotton
fabric shirt much leveland and fabric is not as important as the color of the shirt.
So here are a sample rate of importance that you assign to these features.
You give way of seven to the shirt color.
I have replaced the value of W1 with this number seven.
We assign a rate of four to sleeve land and a rate of two to do fabric.
Finally, we also take a threshold value of it to decide whether two parties per shirt or not.
With these choices of weights and trishul, let's see which of these t shirt would we buy.
So for this Bush shirt, we have blue in the first column, which signifies the color of the shirt.
It is half sleeved.
So half in the second column.
It is non Gordon so non Gordon deterred column.
We just bought fabric.
The fourth column is for calculation of some, as I told you previously.
We calculate the product of weights with the features, add them together to find the calculated sum
in the Fifth Column.
We have written the threshold value that is predicated on the sixth column.
We compare this some value with on value.
If the sum is greater than threshold, we will buy the shirt.
If the sum is less than threshold, we will not buy a t shirt.
So let's let's see what happens with this fourth shirt.
The first shirt is blue in color, but blue.
We have x1 value of one for not blue.
It would have been zero.
So x1 is one x2.
We just leave is zero because it is half slimmed.
Fabric is non Gordon which is again zero.
We find out the same product seven.
It is weightage for color multiplied by the value of X1, which is one plus four.
We did four sleeves multiplied by the value of X2, which is zero because it is half sleeved.
Plus two, which is very dated for fabric multiplied by the value of fabric, which is zero because
it is not codon.
The final time we get is seven.
We compare this some value with the threshold value, which is eight.
The sum is less than eight.
So we are not going to buy this shirt.
Let's do this activity for the second shirt.
The second child is blue coloured balls leaved non-current fabric.
If you repeat the calculation, the only difference is going to be the value of X2 for Wolseley.
Chad X2 will be one.
So seven plus four is going to come out.
That's eleven.
Eleven is more than eight.
So we are going to buy the shirt.
Similarly for the tortured, which is not blue Wolseley cotton shirt, some comes out to be six, which
is less than eight, which means that we are not going to buy this shirt.
Can, you know, see how Perceptron is deciding the Allport that whether you will buy a shirt or not?
It is just multiplying the values of the feature with corresponding weight and taking this sum against
the threshold value.
If the sum is larger than the threshold, it gives one output.
If it is smaller, then it gives other output.
This is a very simple example which I have given to make you understand how a Perceptron is working.
In reality, we solve much more complex problems in which we have numerous input variables and many
conditions.
We will get to them in the due course of the lectures.
As you can see, a Perceptron requires these beads and this Trishul value to give out an output.
And how will Perceptron get devalues of these parameters?
One is we give the values, in which case it is not learning.
It is simple programming the other ways learning where we provide de Perceptron with historical data
of which shirts were selected and which shirts were rejected.
And Perceptron decides to debate and.
This sure value, according to that previous data, might be you can get different models, but changing
very into.
Sure.
For example, if you want to select a shirt, which is blue food sleeved and Gordon only and no other
combination.
And this set of weight and threshold ensures that right out you will see only blue korten full sleeve
shirt will be selected and no other shirt will pass through.
So that's about it.
This is a basic introduction to the Perceptron.
We will extend this idea of Perceptron in the next lecture.