Let's start with perceptions.
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 perception perception was the lab in 1950s.
Yes the work on neural networks began nearly 70 years ago.
Today we use other models of artificial neurons such as sigmoid neurons but to understand take my neurons
we need to first look at perception.
Here's a simple pictorial representation of how perception works.
Perception is this circle or a black box which takes in several binary inputs x1 x2 x3 and so on.
DL exam and produces a single binary output represented by byte 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 by one of the rule is
that we will multiply each of these input values with weight W1 W2 W3 and then compare if the final
value of the sum of these products is greater than a threshold value or not.
If the sum value is greater than the Perseid drone gives an output value of 1.
And if it is less than say sure it gives out an output value of 0.
Mathematically this is how we represent this logic.
This is the summation of weight with feature values.
Basically this means X1 input w 1 plus X2 into W2 plus x3 into W3 and so on the exam into W M the sum
of all these products is this left random.
We compared this sum the threshold value.
If this is less than the threshold we give an output of zero.
If it is more than the threshold we give output of 1
Let's take a simple example which may not be very realistic but you will get the idea of how this perception
functions.
Let's say you want to decide whether you should parties 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 sleeved 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 0.
If it is not blue x2 is equal to 1.
If it is fully believed and 0 if it is half sleeve and extra is equal to 1 for cotton fabric and 0 for
non cotton fabric.
Now suppose that you absolutely adored blue collared shirt and you would prefer food sleeved cotton
fabric shirt much Cleveland 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 weight of 7 to the shirt color.
I have replaced the value of w 1 with this number 7.
We assign a weight of 4 to Cleveland and a weight of 2 to the fabric.
Finally we also take a threshold value of 8 to decide whether two parties pressured or not.
With these choices of weights and threshold.
Let's see which of these three showed would we buy.
So far this first shirt we have blue in the first column which signifies the color of the shirt.
It is half sleeved so half and the second column it is non garden so non Gordon.
The third column which is what fabric the fourth column is for calculation of some.
As I told you previously we calculate the product of weights the features.
Add them together to find we calculated some in the Fifth Column.
We have written the threshold value that is pre decided in the sixth column.
We compare this some value with detail shown value.
If the sum is greater than threshold we will buy a t shirt.
If the sum is less than threshold we will not buy a t shirt.
So let let's see what happens with this.
Shirt.
The fresh shirt is blue in color but blue.
We have X1 value of 1 for not blue.
It would have been 0 so X1 is 1 x2.
We just leave is zero because it is half sleep.
Fabric is non cotton which is again zero.
We find out the same product.
7 It is rated for color multiplied by the value of X 1 which is one plus 4 wear where for sleeves multiplied
by the value of x2 which is zero because it is half sleep plus 2 which is weighted for fabric multiplied
by the value of fabric which is zero because it is not cotton.
The final time we get is 7.
We compare this some value with the threshold value which is 8.
The sum is less than 8.
So we are not going to buy this shirt.
Let us do this activity for the second shirt.
The second shirt is blue colored full sleeved non Gordon fabric.
If you repeat the calculation the only difference is going to be the value of x2 for the full sleeve
shirt.
Extra will be 1 so 7 plus 4 is going to come out as eleven eleven is more than eight.
So we are going to buy this shirt.
Similarly for the third shirt which is not blue full sleeved cotton shirt.
Some comes out to be 6 which is less than 8 which means that we are not going to buy this shirt.
Can you now see how perception is deciding the output that whether you will buy a shirt or not.
It is just multiplying the values of the feature with corresponding weight and checking some 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 perception 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 perception requires these weights and this threshold value to give out an output.
And how will perception get the values of these parameters.
One ways we give the values in which case it is not learning.
It is simple programming.
The other ways learning when we provide deeper separation with historical data of which shots were selected
and which shirts were rejected and the perception decides debate and traditional value according to
that previous data.
By the way you can get different models by changing weights and to be sure.
For example if you want to select a shirt which is blue food sleeved and cotton only and no other combination.
This set of weight and threshold ensures that right out you will see only blue cotton 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 perception we will extend this idea of perception in the next lecture.