 The number of layers is not the only feature of deep nets that explain their superior abilities. An emerging consensus is that many tasks that are hard to learn are characterized by the presence of “nuisance parameters”, sources of variation in input signals that are not correlated with decision success. Examples of nuisance parameters in visual categorization tasks include pose, size, and position in the visual field; examples in auditory tasks include tone, pitch, and duration. Successful systems must learn to recognize deeper similarities hiding under this variation to identify objects in images, or words in audio data.