A neural network consists of many simple processing units that are connected by communication channels. Much of the inspiration for the field of neural networks came from the desire to perform artificial systems capable of sophisticated, perhaps intelligent computations similar to those of the human brain.
Neural networks usually learn from examples and exhibit some capability for generalization beyond the data used for training. They are able to approximate highly nonlinear functional relationships in data sets.
The smallest part of a neural network is one single neuron as shown in
Figure 8.1. It takes a set of individual inputs
and determines (through the learning algorithm)
the optimal connection weights
that
are appropriate to each input.
Next, the neuron
aggregates these weighted values to a single value
A neural network with one hidden layer (single hidden layer) consists of neurons of three basic types: