Technologies

What are Neural Networks?

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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