In deep learning, a ______________ layer is used to reduce the spatial dimensions of the input volume through subsampling.

In deep learning, a pooling layer is used to reduce the spatial dimensions of the input volume through subsampling. Pooling layers help to down-sample the feature maps, which reduces the number of parameters and computations in the network, helping to control overfitting. The most common types of pooling are max pooling, which takes the maximum value from a patch of the feature map, and average pooling, which takes the average value from the patch. By effectively summarizing the features in the patch, pooling layers provide a form of translational invariance and help to maintain the most important information while discarding less significant features.