Which layer is not typically included in convolutional neural networks?

In convolutional neural networks (CNNs), there are specific layers that are commonly utilized to process and analyze visual data, such as images. The correct choice highlights the fact that the recurrent layer is not typically part of the architecture of a CNN.

Convolutional layers, pooling layers, fully-connected layers, and output layers are integral components of CNNs. Convolutional layers apply filters to extract features from input images, pooling layers downsample the feature maps to reduce dimensionality while retaining significant information, fully-connected layers connect every neuron in one layer to every neuron in the next layer to make final classifications, and output layers produce the final probabilities or classifications based on the learned features.

In contrast, recurrent layers are characteristic of recurrent neural networks (RNNs), which are designed to handle sequential data and time-series tasks by maintaining memory of previous inputs. Therefore, while CNNs excel in spatial data tasks like image classification, RNNs are more suited for applications such as natural language processing and time-dependent data analysis. This specialization underscores why the recurrent layer does not belong in the typical structure of convolutional neural networks.