Model Training

Adjusting a model’s weights to match data.

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Training adjusts model parameters so prediction errors decrease.

• Process: Data → prediction → error measurement → weight update.
• Concepts: Learning rate, overfitting, validation, test set, checkpoints.
• Practice: Good data splits, metric monitoring, reproducible pipelines.