Editing Neural network (machine learning) (section)

===Stochastic neural network===

'''Stochastic neural networks''' originating from [[Spin glass#Sherrington–Kirkpatrick model|Sherrington–Kirkpatrick model]]s are a type of artificial neural network built by introducing random variations into the network, either by giving the network's artificial neurons [[Stochastic process|stochastic]] transfer functions {{Citation needed|date=September 2024}}, or by giving them stochastic weights. This makes them useful tools for [[Optimization (mathematics)|optimization]] problems, since the random fluctuations help the network escape from [[Maxima and minima|local minima]].<ref>{{citation|title=Stochastic Models of Neural Networks|volume=102|series=Frontiers in artificial intelligence and applications: Knowledge-based intelligent engineering systems|first=Claudio|last=Turchetti|publisher=IOS Press|year=2004|isbn=978-1-58603-388-0}}</ref> Stochastic neural networks trained using a [[Bayes' theorem|Bayesian]] approach are known as '''Bayesian neural networks'''.<ref>{{Cite magazine |last1=Jospin |first1=Laurent Valentin |last2=Laga |first2=Hamid |last3=Boussaid |first3=Farid |last4=Buntine |first4=Wray |last5=Bennamoun |first5=Mohammed |date=2022 |title=Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users |magazine=IEEE Computational Intelligence Magazine |volume=17 |issue=2 |pages=29–48 |doi=10.1109/mci.2022.3155327 |arxiv=2007.06823 |s2cid=220514248 |issn=1556-603X}}</ref>