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Thus, combining deep and wide models (Figure 11.8) has been shown to improve the performance of models on a wide range of tasks. Deep models are characterized by having many hidden layers and are adept at learning complex relationships between input and output.

In contrast, wide models are designed to learn simple patterns in the data. By combining the two, it is possible to capture both the complex relationships and the simple patterns, leading to more robust and flexible models.

Figure 11.8: Architecture of deep and wide models

Incorporating exceptions into the training process is crucial for better generalization of models to new and unseen data. For example, a language model that is trained only on data that includes one meaning of a word may struggle to recognize other meanings when it encounters them in new data. By incorporating exceptions, the model can learn to recognize multiple meanings of a word, which can improve its performance on a...