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Neural networks are data analysis methods and algorithms loosely
based on nervous systems of humans and animals.
In general terms, an artificial neural network consists of a large
number of simple processing units linked by weighted connections.
By analogy, the processing units may be called neurons. Each unit
receives inputs from many other units and generates a single output.
The
output acts as an input to other processing units. The power of
neural network comes from the combination of many units in a network.
A certain network may be tuned to solve a particular problem by
varying the connection topology and values of the connecting weights
between units.
An
artificial neural network is nonlinear in nature and thus is the
exceptionally powerful method of analyzing real-world data that
allows modeling extremely difficult dependencies.
Neural
nets are proven to be among the best methods to detect hidden relations
in a dataset (e.g. stock market data or sales data). Once a neural
network has analyzed your dataset (this process is called network
training), it is able to make predictions, pattern recognition and
categorization based on these found hidden dependencies.
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