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These applications range from [[stochastic optimization]] methods and algorithms,
to online forms of the [[Expectation–maximization algorithm| EM algorithm]], reinforcement learning
via [[Temporal difference learning|temporal differences]], and [[deep learning]], and others.<ref name=":1">{{cite journal |last1=Toulis |first1=Panos |first2=Edoardo |last2=Airoldi|title=Scalable estimation strategies based on stochastic approximations: classical results and new insights |journal=Statistics and Computing |volume=25 |issue=4 |year=2015 |pages=
Stochastic approximation algorithms have also been used in the social sciences to describe collective dynamics: fictitious play in learning theory and consensus algorithms can be studied using their theory.<ref>{{cite web|last1=Le Ny|first1=Jerome|title=Introduction to Stochastic Approximation Algorithms|url=http://www.professeurs.polymtl.ca/jerome.le-ny/teaching/DP_fall09/notes/lec11_SA.pdf|website=Polytechnique Montreal|publisher=Teaching Notes|accessdate=16 November 2016}}</ref>
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