Dimensionality reduction is a transformation that reduces the dimensionality of the data representation. For example: in the autoencoder network, data is often forced to pass through a "bottleneck" (the end of the encoder and beginning of the decoder) of lower dimension than the input; the representation at the bottleneck is a "compressed" representation of the input, and therefore the encoder can be used -- after training -- for dimensionality reduction.