Basics of Neural networks

 Neural networks

 Imagine a group of people who have never seen " KOALA" (an animal) in their life!!!

So now our job is to teach them if any image is of "KOALA" or not.... We create a team and ask each and every student to work and study each feature of the koala, such as Mike can work on detecting eyes, Jyoti works on forelimbs, Chen can work on hindlimbs, and Mohan can work on the nose.


So they make assumptions by using a score of 0 to 1. For example, where 0 means definitely not Koala's eyes, 0.5 means maybe or maybe not Koala's eyes and 1 means definitely Koala's eyes.
Then Serena, their superior notes down their observations and based on a formula tells us finally if an animal is a Koala or not.
 
This is nothing but a neural network. Each individual person here is an individual neuron. They're working on a particular subtask and pass the result of their subtask to the next group. Here, Serena and Nidhi are the hidden layer, Mike, Mohan, etc forming the input layer or the first layer and Sergey is the output layer. 
Say for example, if the given image is not a Koala but the group comes to a conclusion that is a Koala then Sergey the head of the group goes to the teacher to tell the answer which is 'NO'..the teacher then corrects him that the given image is a Koala! So Sergey goes back to the class and passes the information to Nidhi and Serena and then they both further pass it down to the rest of the classroom. This is known as "Backward Error Propagation."



















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