Deep Learning bezeichnet eine Klasse von Optimierungsmethoden künstlicher neuronaler Netze, die zahlreiche verborgene Schichten (hidden layers) zwischen Eingabeschicht und Ausgabeschicht haben und dadurch eine umfangreiche innere Struktur aufweisen.

Als Erweiterung der Lernalgorithmen für Netzstrukturen mit sehr wenigen oder keinen verborgenen Schichten, ermöglichen die Methoden des Deep Learnings auch bei zahlreichen verborgenen Schichten einen stabilen Lernerfolg.

ETSI GR ENI 004 use of hierarchical computational models, which are composed of multiple processing layers, to learn representations of data with multiple levels of abstraction

NOTE: This replaces manually-intensive processes, and enables a machine to both learn features and use them to perform a task. Deep learning can be applied to almost any of the other algorithms defined here, as long as there are at least two hidden layers.

ISO/IEC DIS 22989 approach to creating rich hierarchical representations through the training (3.2.21) of neural networks (3.3.7) with many hidden layers.

ISO/IEC TR 29119-11:2020 approach to creating rich hierarchical representations through the training of neural networks (3.1.48) with one or more hidden layers

Note 1 to entry: Deep learning uses multi-layered networks of simple computing units (or “neurons”). In these neural networks each unit combines a set of input values to produce an output value, which in turn is passed on to other neurons downstream.

ISTQB - CTAI Syllabus ML using neural networks with multiple layers