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Core
Numerical Stability in Code
Numerical stability is writing arithmetic so floating-point error and overflow do not corrupt the result, which matters because naive ML math (softmax, cross-entropy, variance) silently returns NaN or wrong gradients. Applied AI interviews probe it because the fixes (log-sum-exp, max-subtraction, working in log-space) are small code changes that separate engineers who have shipped training loops from those who have only called library functions.
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