Consider the following mathematical expression which represents the goal of training a predictive model: $\hat{\theta} = \arg \min_{\theta} \sum_{\mathbf{x} \in D} \text{Loss}_{\theta}(\mathbf{x})$ Explain the role of each of the following three components in this expression: (1) the summation symbol ($\sum_{\mathbf{x} \in D}$), (2) the $\text{Loss}_{\theta}(\mathbf{x})$ term, and (3) the $\arg \min_{\theta}$ operator. How do they work together to define the training process?