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Let's start with perceptions.

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Like in biology.

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A single cell of our nervous system is called a neuron in artificial neural networks.

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One of the earliest such artificial neuron was a perception perception was the lab in 1950s.

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Yes the work on neural networks began nearly 70 years ago.

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Today we use other models of artificial neurons such as sigmoid neurons but to understand take my neurons

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we need to first look at perception.

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Here's a simple pictorial representation of how perception works.

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Perception is this circle or a black box which takes in several binary inputs x1 x2 x3 and so on.

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DL exam and produces a single binary output represented by byte by binary input and binary output.

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I mean that these variables can only take two values for example zero and one true or false etc..

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There are several ways in which these x1 x2 x3 can give us the desired output by one of the rule is

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that we will multiply each of these input values with weight W1 W2 W3 and then compare if the final

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value of the sum of these products is greater than a threshold value or not.

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If the sum value is greater than the Perseid drone gives an output value of 1.

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And if it is less than say sure it gives out an output value of 0.

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Mathematically this is how we represent this logic.

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This is the summation of weight with feature values.

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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

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of all these products is this left random.

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We compared this sum the threshold value.

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If this is less than the threshold we give an output of zero.

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If it is more than the threshold we give output of 1

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Let's take a simple example which may not be very realistic but you will get the idea of how this perception

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functions.

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Let's say you want to decide whether you should parties is a particular shirt or not.

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You might make your decision by weighing up three factors whether this shirt is blue or not whether

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the shirt is falsely or half sleeved and whether the fabric is cotton or not we can represent these

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three variables using three binary variables.

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For instance X1 is equal to one.

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If the shirt is blue and it is 0.

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If it is not blue x2 is equal to 1.

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If it is fully believed and 0 if it is half sleeve and extra is equal to 1 for cotton fabric and 0 for

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non cotton fabric.

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Now suppose that you absolutely adored blue collared shirt and you would prefer food sleeved cotton

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fabric shirt much Cleveland and fabric is not as important as the color of the shirt.

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So here are a sample rate of importance that you assign to these features.

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You give weight of 7 to the shirt color.

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I have replaced the value of w 1 with this number 7.

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We assign a weight of 4 to Cleveland and a weight of 2 to the fabric.

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Finally we also take a threshold value of 8 to decide whether two parties pressured or not.

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With these choices of weights and threshold.

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Let's see which of these three showed would we buy.

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So far this first shirt we have blue in the first column which signifies the color of the shirt.

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It is half sleeved so half and the second column it is non garden so non Gordon.

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The third column which is what fabric the fourth column is for calculation of some.

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As I told you previously we calculate the product of weights the features.

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Add them together to find we calculated some in the Fifth Column.

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We have written the threshold value that is pre decided in the sixth column.

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We compare this some value with detail shown value.

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If the sum is greater than threshold we will buy a t shirt.

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If the sum is less than threshold we will not buy a t shirt.

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So let let's see what happens with this.

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Shirt.

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The fresh shirt is blue in color but blue.

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We have X1 value of 1 for not blue.

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It would have been 0 so X1 is 1 x2.

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We just leave is zero because it is half sleep.

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Fabric is non cotton which is again zero.

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We find out the same product.

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7 It is rated for color multiplied by the value of X 1 which is one plus 4 wear where for sleeves multiplied

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by the value of x2 which is zero because it is half sleep plus 2 which is weighted for fabric multiplied

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by the value of fabric which is zero because it is not cotton.

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The final time we get is 7.

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We compare this some value with the threshold value which is 8.

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The sum is less than 8.

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So we are not going to buy this shirt.

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Let us do this activity for the second shirt.

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The second shirt is blue colored full sleeved non Gordon fabric.

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If you repeat the calculation the only difference is going to be the value of x2 for the full sleeve

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shirt.

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Extra will be 1 so 7 plus 4 is going to come out as eleven eleven is more than eight.

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So we are going to buy this shirt.

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Similarly for the third shirt which is not blue full sleeved cotton shirt.

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Some comes out to be 6 which is less than 8 which means that we are not going to buy this shirt.

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Can you now see how perception is deciding the output that whether you will buy a shirt or not.

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It is just multiplying the values of the feature with corresponding weight and checking some against

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the threshold value if the sum is larger than the threshold.

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It gives one output if it is smaller then it gives other output.

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This is a very simple example which I have given to make you understand how a perception is working.

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In reality we solve much more complex problems in which we have numerous input variables and many conditions.

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We will get to them in the due course of the lectures.

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As you can see a perception requires these weights and this threshold value to give out an output.

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And how will perception get the values of these parameters.

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One ways we give the values in which case it is not learning.

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It is simple programming.

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The other ways learning when we provide deeper separation with historical data of which shots were selected

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and which shirts were rejected and the perception decides debate and traditional value according to

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that previous data.

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By the way you can get different models by changing weights and to be sure.

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For example if you want to select a shirt which is blue food sleeved and cotton only and no other combination.

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This set of weight and threshold ensures that right out you will see only blue cotton full sleeve shirt

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will be selected and no other shirt will pass through.

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So that's about it.

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This is a basic introduction to the perception we will extend this idea of perception in the next lecture.