Conclusion

Evaluation and debugging make model results inspectable.

The chapter's core message is practical: do not trust a final number without checking how it was produced.

You have seen how to inspect:

• train, validation, and test split roles
• task-appropriate metrics
• learning curves
• gradient norms
• activation statistics
• parameter histograms
• dead ReLUs
• sanity checks and seed sensitivity

The next chapter begins the bridge toward language modeling. Embeddings show how discrete items can become learned vectors.