Neural Networks Backpropagation tutorial
A Neural Network (or artificial neural network) is a collection of interconnected processing elements or nodes. The nodes are termed simulated neurons as they attempt to imitate the functions of biological neurons. The nodes are connected together via links. We can compare this with axon-synapse-dendrite connections in the human brain.
Initially, a weight is assigned at random to each link in order to determine the strength of one node’s influence on the other. When the sum of input values reaches a threshold value, the node will produce the output 1 or 0 otherwise. By adjusting the weights the desired output can be obtained. This training process makes the network learn. The network, in other words, acquires knowledge in much the same way human brains acquire namely learning from experience. Backpropagation is one of the powerful artificial neural network technique which is used acquire knowledge automatically.
Backpropagation method is the basis for training a supervised neural network. The output of Backpropagation network is a real value which lies between 0 and 1 based on the sigmoid function. The formula for the output is,
Output = 1 / (1+e-sum)
As the sum increases, the output approaches 1. As the sum decreases, the output approaches 0.
Source: Artificial Intelligence
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