Editing Artificial intelligence (section)

=== Artificial neural networks ===
[[File:Artificial_neural_network.svg|right|thumb|A neural network is an interconnected group of nodes, akin to the vast network of [[neuron]]s in the [[human brain]].]]

An artificial neural network is based on a collection of nodes also known as [[artificial neurons]], which loosely model the [[neurons]] in a biological brain. It is trained to recognise patterns; once trained, it can recognise those patterns in fresh data. There is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the [[Weighting|weight]] crosses its specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers.<ref name="Neural networks">Neural networks: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 21}}, {{Harvtxt|Domingos|2015|loc=Chapter 4}}</ref>

Learning algorithms for neural networks use [[local search (optimization)|local search]] to choose the weights that will get the right output for each input during training. The most common training technique is the [[backpropagation]] algorithm.<ref>Gradient calculation in computational graphs, [[backpropagation]], [[automatic differentiation]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 21.2}}, {{Harvtxt|Luger|Stubblefield|2004|pp=467–474}}, {{Harvtxt|Nilsson|1998|loc=chpt. 3.3}}</ref> Neural networks learn to model complex relationships between inputs and outputs and [[Pattern recognition|find patterns]] in data. In theory, a neural network can learn any function.<ref>[[Universal approximation theorem]]: {{Harvtxt|Russell|Norvig|2021|p=752}}
The theorem: {{Harvtxt|Cybenko|1988}}, {{Harvtxt|Hornik|Stinchcombe|White|1989}}</ref>

In [[feedforward neural network]]s the signal passes in only one direction.<ref>[[Feedforward neural network]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 21.1}}</ref> [[Recurrent neural network]]s feed the output signal back into the input, which allows short-term memories of previous input events. [[Long short term memory]] is the most successful network architecture for recurrent networks.<ref>[[Recurrent neural network]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 21.6}}</ref> [[Perceptron]]s<ref>[[Perceptron]]s: {{Harvtxt|Russell|Norvig|2021|pp=21, 22, 683, 22}}</ref> use only a single layer of neurons; deep learning<ref name="Deep learning"/> uses multiple layers. [[Convolutional neural network]]s strengthen the connection between neurons that are "close" to each other—this is especially important in [[image processing]], where a local set of neurons must [[edge detection|identify an "edge"]] before the network can identify an object.<ref>[[Convolutional neural networks]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 21.3}}</ref>

{{Clear}}