A machine learning model is defined by the function $F_{\omega, \hat{\theta}}(x)$, where $\omega$ represents the fixed architectural parameters (e.g., number of layers) and $\hat{\theta}$ represents the parameters learned from data (e.g., connection weights). The model is initially trained on a dataset of images of cats. If the exact same model architecture is then retrained from scratch on a new dataset of images of dogs, which component of the notation is expected to have a new value as a direct result of this new training process?

Deconstructing a Model's Function

Mapping a Spam Filter to Functional Notation