 One of the most commonly-deployed deep architectures—deep convolutional networks—leverages a combination of strategies that are well-suited to overcoming nuisance variation. Golden Age nets used the same activation function for all units, and units in a layer were fully connected to units in adjacent layers. However, deep convolutional nets deploy several different activation functions, and connections to units in the next higher layer are restricted to small windows, such as a square tile of an image or a temporal snippet of a sound file.