Editing Neural network (machine learning) (section)

{{short description|Computational model used in machine learning, based on connected, hierarchical functions}}{{cs1 config|name-list-style=vanc|display-authors=6}}
{{about|the computational models used for artificial intelligence||Neural network (disambiguation)}}
{{Use dmy dates|date=March 2023}}
[[File:Colored neural network.svg|thumb|upright=1.15|An artificial neural network is an interconnected group of nodes, inspired by a simplification of [[neuron]]s in a [[brain]]. Here, each circular node represents an [[artificial neuron]] and an arrow represents a connection from the output of one artificial neuron to the input of another.]]
In [[machine learning]], a '''neural network''' (also '''artificial neural network''' or '''neural net''', abbreviated '''ANN''' or '''NN''') is a computational model inspired by the structure and functions of biological neural networks.<ref>{{cite web|last=Hardesty|first=Larry|title=Explained: Neural networks|date=14 April 2017|url=https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414|publisher=MIT News Office|access-date=2 June 2022|archive-date=18 March 2024|archive-url=https://web.archive.org/web/20240318120205/https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414|url-status=live}}</ref><ref>{{cite book |last1=Yang |first1=Z.R. |last2=Yang |first2=Z. |title=Comprehensive Biomedical Physics |date=2014 |publisher=Elsevier |location=Karolinska Institute, Stockholm, Sweden |isbn=978-0-444-53633-4 |page=1 |url=https://www.sciencedirect.com/topics/neuroscience/artificial-neural-network |access-date=28 July 2022 |archive-date=28 July 2022 |archive-url=https://web.archive.org/web/20220728183237/https://www.sciencedirect.com/topics/neuroscience/artificial-neural-network |url-status=live }}</ref>

A neural network consists of connected units or nodes called ''[[artificial neuron]]s'', which loosely model the [[neuron]]s in the brain. Artificial neuron models that mimic biological neurons more closely have also been recently investigated and shown to significantly improve performance. These are connected by ''edges'', which model the [[synapse]]s in the brain. Each artificial neuron receives signals from connected neurons, then processes them and sends a signal to other connected neurons. The "signal" is a [[real number]], and the output of each neuron is computed by some non-linear function of the sum of its inputs, called the ''[[activation function]]''. The strength of the signal at each connection is determined by a ''[[Weighting|weight]]'', which adjusts during the learning process.

Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the ''input layer'') to the last layer (the ''output layer''), possibly passing through multiple intermediate layers (''[[hidden layer]]s''). A network is typically called a deep neural network if it has at least two hidden layers.<ref>{{Cite book |last=Bishop |first=Christopher M. |title=Pattern Recognition and Machine Learning |date=17 August 2006 |publisher=Springer |isbn=978-0-387-31073-2 |location=New York |language=en}}</ref>

Artificial neural networks are used for various tasks, including [[predictive modeling]], [[adaptive control]], and solving problems in [[artificial intelligence]]. They can learn from experience, and can derive conclusions from a complex and seemingly unrelated set of information.

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