!!! Overview
[{$pagename}]  (also known as deep structured learning or [hierarchical] [learning]) is part of a broader family of [Machine Learning] methods based on [learning] [data] representations, as opposed to task-specific [algorithms]. 

[{$pagename}] can be [Supervised Learning], partially supervised or [unsupervised Learning].

[{$pagename}] [architectures] such as [Deep Neural networks], [Deep Belief networks] and [Recurrent Neural networks] have been applied to fields including [computer vision], [speech recognition], [Natural Language Processing], [Sound recognition], [social Websites] filtering, machine translation, [bioinformatics] and drug design, where they have produced results comparable to and in some cases superior to human experts.

[{$pagename}] is a class of [Machine Learning] [algorithms] that:
* use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input.
* learn in [supervised|Supervised Learning] (e.g., [classification]) and/or [Unsupervised Learning] (e.g., pattern analysis) manners.
* learn multiple levels of representations that correspond to different levels of abstraction; the levels form a [hierarchy] of concepts.
* use some form of [gradient descent] for training via [backpropagation].
Layers that have been used in [Deep Learning] include [hidden nodes] of an [Artificial Neural network] and sets of propositional formulas.

[{$pagename}] may also include latent [variable]s organized layer-wise in deep generative models such as the nodes in [Deep Belief Networks] and [Deep Boltzmann Machines].

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


!! More Information
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* [#1] - [Deep_learning|Wikipedia:Deep_learning|target='_blank'] - based on information obtained 2017-11-24-