Figure 4.

Construction of a deep model M from data (D) using a learner ($\mathcal{L}$). We use $\pi$ to denote the structure (organisation of various layers, their interconnections etc.) and $\mathit{\theta }$ to denote the parameters (synaptic weights) of the deep network. L denotes the loss function (for example, cross-entropy loss in case of classification).