neural networks

Traditionally, the term neural network had been used to refer to a network or circuitry of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term 'Neural Network' has two distinct connotations:Biological neural networks are made up of real biological neurons that are connected or functionally-related in the peripheral nervous system or the central nervous system. In the field of neuroscience, they are often identified as groups of neurons that perform a specific physiological function in laboratory analysis.Artificial neural networks are made up of interconnecting artificial neurons (usually simplified neurons) which may share some properties of biological neural networks. Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving traditional artificial intelligence tasks without necessarily attempting to model a real biological system.
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Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data.

For more information, see Neural Networks; see also Data Mining, and STATISTICA Neural Networks.

Algorithms which calculate the average gradient over an epoch, rather than adjusting on a case-by-case basis during training. Quick propagation, Delta-Bar-Delta, conjugate gradient descent and Levenberg-Marquardt are all batch algorithms.