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.
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
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.
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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