**Depth of a network**

The number of layers in the network.

**Feature vector / representation / volume**

A three dimensional tensor of size obtained in a certain layer of a neural network. W is the width, H is the height and D is the depth, i.e., the number of channels. If there is more than one example, this becomes a four dimensional tensor of size , where is the batch size. image source

**Spatial invariant feature vector**

A feature vector that remains unchanged even if the input to the network is spatially translated.

**Filters and biases**

Filters are a four dimensional tensor of size and biases are a vector of length . is the width and height of the filter, is the number of channels and is the number of filters.

**Neighbourhood**

A group of consecutive entries in a two-dimensional signal that has a rectangular or a square shape. image source