!!! Overview
[{$pagename}] (DNN) is an [Artificial Neural network] with multiple [hidden layer]s between the [input layer] and [output layer]s.

Similar to shallow [Artificial Neural networks], [{$pagename}]s can model complex non-linear relationships. 

[{$pagename}] [architectures] generate compositional models where the object is expressed as a layered composition of primitives. The extra layers enable composition of features from lower layers, potentially modeling complex [data] with fewer units than a similarly performing shallow network.

[{$pagename}] architectures include many variants of a few basic approaches. Each architecture has found success in specific domains. It is not always possible to compare the performance of multiple architectures, unless they have been evaluated on the same data sets.

[{$pagename}]s are typically [Feedforward Neural networks] in which data flows from the input layer to the output layer without looping back.

[Recurrent Neural networks] ([RNNs]), in which data can flow in any direction, are used for applications such as language modeling. Long short-term memory is particularly effective for this use.

[Convolutional Neural Network] (CNNs) are used in [computer vision].  CNNs also have been applied to [audio] modeling for automatic [speech recognition] (ASR).

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

!! More Information
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* [#1] - [Deep_learning|Wikipedia:Deep_learning|target='_blank'] - based on information obtained 2017-11-29-