Editing Neural network (machine learning) (section)

=== Organization ===
The neurons are typically organized into multiple layers, especially in deep learning. Neurons of one layer connect only to neurons of the immediately preceding and immediately following layers. The layer that receives external data is the ''input layer''. The layer that produces the ultimate result is the ''output layer''. In between them are zero or more ''hidden layers''. Single layer and unlayered networks are also used. Between two layers, multiple connection patterns are possible. They can be 'fully connected', with every neuron in one layer connecting to every neuron in the next layer. They can be ''pooling'', where a group of neurons in one layer connects to a single neuron in the next layer, thereby reducing the number of neurons in that layer.<ref name="flexible">{{cite journal|last=Ciresan|first=Dan|author2=Ueli Meier|author3=Jonathan Masci|author4=Luca M. Gambardella|author5=Jurgen Schmidhuber|year=2011|title=Flexible, High Performance Convolutional Neural Networks for Image Classification|url=https://people.idsia.ch/~juergen/ijcai2011.pdf|journal=Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence-Volume Volume Two|volume=2|pages=1237–1242|access-date=7 July 2022|url-status=live|archive-url=https://web.archive.org/web/20220405190128/https://people.idsia.ch/~juergen/ijcai2011.pdf|archive-date=5 April 2022}}</ref> Neurons with only such connections form a [[directed acyclic graph]] and are known as [[feedforward neural network|''feedforward networks'']].<ref name="Zell1994p73">{{cite book|title=Simulation Neuronaler Netze|last=Zell|first=Andreas|publisher=Addison-Wesley|year=1994|isbn=3-89319-554-8|edition=1st|page=73|language=de|trans-title=Simulation of Neural Networks}}</ref> Alternatively, networks that allow connections between neurons in the same or previous layers are known as [[Recurrent neural network|''recurrent networks'']].<ref>{{Cite journal|last=Miljanovic|first=Milos|date=February–March 2012|title=Comparative analysis of Recurrent and Finite Impulse Response Neural Networks in Time Series Prediction|url=http://www.ijcse.com/docs/INDJCSE12-03-01-028.pdf|journal=Indian Journal of Computer and Engineering|volume=3|issue=1|access-date=21 August 2019|archive-date=19 May 2024|archive-url=https://web.archive.org/web/20240519081156/http://www.ijcse.com/docs/INDJCSE12-03-01-028.pdf|url-status=live}}</ref>