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The model's learned weights and parameters can be described as the numerical values that determine how the model behaves when processing input data. These values are typically adjusted through the training process, where the model is shown a set of inputs and corresponding desired outputs, and the weights and parameters are updated to minimize the difference between the predicted outputs and actual outputs. The learned weights and parameters essentially define the model and the capabilities it has for making predictions or classifications. They can be thought of as the "knowledge" that the model has gained from the training data.