What are ANN ?

Artificial Neural Networks or shortly ANN ;
Artificial neurons are similar to their biological counterparts. They have input connections which are summed together to determine the strength of their output, which is the result of the sum being fed into an activation function. Though many activation functions exist, the most common is the sigmoid activation function, which outputs a number between 0 (for low input values) and 1 (for high input values). The resultant of this function is then passed as the input to other neurons through more connections, each of which are weighted. These weights determine the behaviour of the network.

When we create ANNs, the neurons are usually ordered in layers with connections going between the layers. The fi rst layer contains the input neurons and the last layer contains the output neurons. These input and output neurons represent the input and output variables of the function that we want to approximate. Between the input and the output layer a number of hidden layers exist and the connections (and weights) to and from these hidden layers determine how well the ANN performs. When an ANN is learning to approximate a function, it is shown examples of how the function works and the internal weights in the ANN are slowly adjusted so as to produce the same output as in the examples. The hope is that when the ANN is shown a new set of input variables, it will give a correct output.
( from Neural Networks Made Simple )

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