Backpropagation Without Autograd
Demonstrate all 3 repaired bugs and defend why a passed gradient check is necessary but not sufficient for generalization.
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Demonstrate all 3 repaired bugs and defend why a passed gradient check is necessary but not sufficient for generalization.
Map each implemented component to the original paper and explain 2 reduced-model failure modes without claiming full-scale reproduction.
Defend a threshold using decision costs and explain why accuracy alone can reward a useless classifier.
Defend a clustering or anomaly decision while stating what cannot be inferred from an unlabeled dataset.
Defend the architecture comparison using learning curves, error categories and the cost of each improvement.
Deliver an operational runbook linking alerts to investigation, intervention and rollback criteria without promising automatic drift correction.
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