Our sense organs interact with the outer world and send the visual and sound information to the neurons, 1
let's say you are looking at this picture and your neurons want to decide. 2
if your reaction would be laugh or not. 3
By looking at this picture, each neuron get fired or activated only when its respective criteria are 4
met. 5
Or we can say each of these neurons have a specific tolerance. 6
So if the tolerance is met, then it would be fired or activated. 7
And then at the result we can decide to laugh or not. 8
But actually, in reality, it's not only a few neurons who decide to laugh to show a reaction to one 9
event. 10
Of course, there are millions of neurons interconnected to Chander and they are making a hierarchical 11
decision based on what they receive. 12
It can be any signal like a visual signal, look, a sound signal, or like other sense signals. 13
Now the sense organs pass the information to the first or lowest layer of neurons to process it, and 14
the outputs of these processes are passed on the next layers in a hierarchical manner. 15
Some of the neurons will fire and some won't. 16
And this process goes on until the result is finally the response, which in this case is. 17
This massive parallel network also ensure that there is a division of work. 18
Each neuron only fired and activated when its specific criteria are met. 19
It is believed that neurons are our age in a hierarchical fashion and each layer has its own role and 20
responsibility. To detect the face, 21
it's not only one neuron to decide whether this is a face or not, but there are hierarchical and different 22
layers of neurons to decide whether it is a face or not. 23
In this example, the first layer, let's say, to take the edge and corners if it criterium met, it 24
will pass to the second layer, which will decide on the form feature of this group's nose, mouth and 25
eyes and the third layer detect the high-level objects, like if this is a face or not and puts everything 26
together. 27
We are using the same criteria. 28
we're using the same method for machines as well to teach them how to detect a specific animal, a specific 29
object or a face. 30
This is an example of deep learning to detect either this animal is an elephant or not. 31
There are several layers. suppose, 32
one layer is for detecting the tail, another one is for detecting the ears. 33
and finally, we can have the full shape of an elephant.