Editing Neural Networks (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert practitioners evaluate neural networks along several dimensions beyond simple accuracy:

'''Generalization gap''': The difference between training accuracy and validation accuracy. A small gap with both high indicates good learning; a large gap indicates overfitting.

'''Learning curve analysis''': Plotting train/validation loss over epochs reveals whether the model is underfitting (both losses remain high) or overfitting (training loss drops but validation loss rises).

'''Ablation studies''': Systematically removing components (dropout, batch norm, skip connections) to understand what each contributes. This is how experts build principled understanding rather than cargo-culting architectures.

'''Calibration''': A well-calibrated model's confidence scores reflect true probability. A model that says "90% confident" should be right ~90% of the time. Poorly calibrated models are dangerous in production. Use temperature scaling or Platt scaling to improve calibration.

Expert practitioners also think carefully about '''inductive biases''' — what assumptions about structure are baked into the architecture. CNNs assume spatial locality and translation invariance. RNNs assume sequential dependencies. Transformers assume pairwise attention relationships. Choosing architectures whose inductive biases match the problem structure is a hallmark of expert design.
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