Neural Networks

Definition of Neural Networks as it relates to Technology, Artificial Intelligence, Deep Learning, Expert Systems

Neural Networks, as applied in Expert Systems, are algorithms intended to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something the output layer can use. They are particularly useful in Expert Systems because they can learn and improve from experience, adjusting their internal parameters to produce better results as more data becomes available. This makes them ideal for tasks such as prediction, classification, and decision-making in complex systems. In the context of Technology, Artificial Intelligence, and Deep Learning, Neural Networks represent a significant advancement in the field of machine learning, enabling the development of sophisticated models that can tackle increasingly challenging problems.

Note
  • [DeepNeuralNetworks.net]
  • [bionn.matinf.uj.edu.pl] Bio-inspired artificial neural networks
  • [snufa.net] SNUFA | Spiking Neural networks as Universal Function Approximators
  • [holger-arndt.com] Holger Arndt | Big Data Analytics, Machine Learning, Neural Networks
  • [rnns.net] RNNS — Official site of Russian Neural Network Society
  • [nnql.org] NNQL Neural Network Query Language
  • [pfr.com] Parallax Financial Research – Neural Network Solutions for Professional Money Managers
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