Neural networks are set of algorithms whose model or whose model is inspired by our human brain neural networks are designed to recognize and distinguish between patterns they interpret sensory data through a kind of machine perception .
Labeling or clustering raw inputs the patterns they recognize are numerical contained in vectors into which data such as images sounds and text must be translated a typical neuron consists of four parts and this is dendrite .
This dendrite they are tree like branches responsible for receiving the information from other neurons it is connected to in other sense we can say that they are like the ears of neurons then comes the soma actually the cell body of the neuron .
And this is responsible for processing of information they have received from these dendrites exon this is just like a cable through which neurons send the information exon and then synapses signup says it is the connection between the axon and the other neuron .
Dendrites so this is how an artificial neural network may be modeled using the analogy and terminology of human brain dendrites from this biological neural network represents the inputs this is the input .
Then cell nucleus represents the nodes let us mark it and then we have synapses and these are going to represent the weights these are the weights and axon will represent the output so now we are ready to emulate the biological neural network with artificial neural .
Network remember what we just decided what will be the dendrites they will be the inputs so let us see here the cell nucleus will be the nodes the synapses will be the weights and the axon will be the output that is dendrites inputs cell nucleus nodes synapse .
Weights an axon will be the output this is just one neuron but in fact is artificial neuron or neural network they are quite huge they have their own architecture so let us understand the architecture first a neural network consists of large number of neurons .
Arranged in these layers sequence of layer so artificial neural network primarily consists of three layers the first one is the input layer this input layer as the name is suggesting it accepts input in several different formats provided by .
The programmer so this is the input layer then we have hidden layers so a hidden layer in between this input layer and the output layer this pump performs all the computation and the calculation right to find the hidden features and .
Patterns then we have the output layer the input goes through the series of transformation here using this hidden layer which finally result in the output that is conveyed using this output layer so here you see an example that how a face or a pattern is .
Recognized through input by the hidden layers and output is being shown here we'll take up real life example also