Deep Learning, refers to ML methods based on artificial neural networks, which have numerous intermediate layers (hidden layers) between input layer and output layer and thus have an extensive inner structure.
As an extensions of the learning algorithms for network structures with very few or no intermediate layers, the methods of deep learning enable a stable learning success even with numerous intermediate layers.
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