!!! Overview
[{$pagename}] ([CNN]) is a class of deep, [feedforward Neural network] that has successfully been applied to analyzing visual imagery.


[{$pagename}] use a variation of multilayer [perceptrons] designed to require minimal preprocessing.

[{$pagename}] are also known as shift invariant or [Space Invariant Artificial Neural Network]s ([SIANN]), based on their shared-weights architecture and translation invariance characteristics.

[{$pagename}]s were inspired by biological processes in which the connectivity pattern between [neurons] is inspired by the organization of the animal visual cortex. Individual cortical [neurons] respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different [neurons] partially overlap such that they cover the entire visual field.

[{$pagename}]s use relatively little pre-processing compared to other image classification algorithms. This means that the [{$pagename}] learns the filters that in traditional algorithms were hand-engineered. This independence from prior knowledge and human effort in feature design is a major advantage.

They have applications in image and video [recognition], recommender systems and natural language processing.

[{$pagename}] has many drivitives:
* multi-delay sync (MDS) network -  to align and predict [emotion] annotations


!! Category
%%category [Artificial Intelligence]%%

!! More Information
There might be more information for this subject on one of the following:
[{ReferringPagesPlugin before='*' after='\n' }]
----
* [#1] - [Convolutional_neural_network|Wikipedia:Convolutional_neural_network|target='_blank'] - based on information obtained 2017-11-24-