MT1B Q1a

Answer — step by step

First, deep networks can learn useful representations directly from complex data such as images and speech, reducing dependence on manually engineered features. Second, multiple nonlinear layers can model complicated patterns and relationships when suitable data, computation and training are available. Example: a CNN can learn edges, parts and object features at successive levels.