Got the basic version down fine but then they pushed on numerical stability and I fumbled.
Start by defining softmax mathematically: for input vector z, softmax(z)_i = exp(z_i) / sum_j exp(z_j). Then implement it with numerical stability by subtracting the maximum value from each element before exponentiation, and discuss trade-offs like handling large inputs and vectorization.
Pro tip: Mention that subtracting the max is a standard trick to prevent overflow and underflow, and that it doesn't change the output mathematically. Also, consider edge cases like empty input or all -inf values.
Ask about input type (vector, matrix, batch), expected output, and any performance or numerical stability requirements.
State the softmax formula and note that it normalizes inputs into a probability distribution.
Subtract the maximum value from each element before exponentiation to avoid overflow/underflow, then compute exponentials and normalize.
Talk about vectorization, handling batches, and potential issues like underflow for very negative inputs.
Verify with simple inputs, large values, and edge cases like all zeros or extreme values to ensure correctness.
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