Instruction: Define both terms and explain how they differ from each other.
Context: This question assesses the candidate's knowledge of the distinctions between model parameters and hyperparameters.
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The way I'd explain it in an interview is this: A parameter is a value the model learns from data during training, such as the weights in a neural network or the coefficients in logistic regression. A hyperparameter is a value you set or search over outside the training loop,...