softmax

Almost everyone can quote softmax(QKᵀ/√d_k)V. The interviewer wants the variance argument and the exact training failure the scale prevents. Here is the answer that separates memorization from understanding.

A deceptively simple ML-coding ask. Anyone can write exp/sum; the signal is the max-subtraction trick and the log-sum-exp form that keep it from overflowing. Here is the stable implementation and why the naive one breaks.

Every chat model's 'temperature' and 'top_p' knobs are a few lines of logit surgery. Implementing them shows you understand decoding, not just call it. Here is the from-scratch version.

A staple that checks whether you know why ReLU dethroned sigmoid and how to match the output activation to the loss. The signal is the vanishing-gradient story told correctly. Here is the answer.

Some models are natively multiclass; others need a wrapper to stretch a binary classifier to K classes. The signal is knowing the three strategies and exactly when each one wins. Here is the answer.