Editing Neural network (machine learning) (section)

===Artificial neurons ===
{{main|Artificial neuron}}
ANNs are composed of [[artificial neurons]] which are conceptually derived from biological [[neuron]]s. Each artificial neuron has inputs and produces a single output which can be sent to multiple other neurons.<ref name="Abbod2007">{{cite journal|last1=Abbod|first1=Maysam F.|year=2007|title=Application of Artificial Intelligence to the Management of Urological Cancer|journal=The Journal of Urology|volume=178|issue=4|pages=1150–1156|doi=10.1016/j.juro.2007.05.122|pmid=17698099}}</ref> The inputs can be the feature values of a sample of external data, such as images or documents, or they can be the outputs of other neurons. The outputs of the final ''output neurons'' of the neural net accomplish the task, such as recognizing an object in an image.{{citation needed|date=October 2024}}

To find the output of the neuron we take the weighted sum of all the inputs, weighted by the ''weights'' of the ''connections'' from the inputs to the neuron. We add a ''bias'' term to this sum.<ref name="DAWSON1998">{{cite journal|last1=Dawson|first1=Christian W.|year=1998|title=An artificial neural network approach to rainfall-runoff modelling|journal=Hydrological Sciences Journal|volume=43|issue=1|pages=47–66|doi=10.1080/02626669809492102|bibcode=1998HydSJ..43...47D |doi-access=free}}</ref> This weighted sum is sometimes called the ''activation''. This weighted sum is then passed through a (usually nonlinear) activation function to produce the output. The initial inputs are external data, such as images and documents. The ultimate outputs accomplish the task, such as recognizing an object in an image.<ref>{{Cite web|url=http://www.cse.unsw.edu.au/~billw/mldict.html#activnfn|title=The Machine Learning Dictionary|website=cse.unsw.edu.au|access-date=4 November 2009|archive-url=https://web.archive.org/web/20180826151959/http://www.cse.unsw.edu.au/~billw/mldict.html#activnfn|archive-date=26 August 2018}}</ref>