softmax
AI, ML & GenAI interview questions tagged softmax, across every topic.
5 questions · 2 unlocked for you
Concepts behind "softmax"
The curriculum that explains the ideas these questions test.
Core
Logits, Log-Probs, and Logit BiasThe output layer of an LLM is an API surface, not just an implementation detail. Logits are raw per-token scores, softmax turns them into probabilities, and log-probs are what providers actually return because they are numerically stable and add up across a sequence. Four production techniques live here: confidence scoring for routing and abstention, logit bias to ban or force a token, structured decoding by masking invalid tokens, and classification by reading a single position's log-probs instead of parsing prose. AI, ML, and GenAI engineer interviews probe it because it is the difference between treating the model as a text box and treating it as a probabilistic component.🧠 Foundations of LLMs & GenAISign in
Core
Implementing ML From Scratch (NumPy Patterns)ML-from-scratch coding rounds check whether you can express a model as vectorized array operations rather than Python loops, lay out a clean forward and backward pass, and write a numerically careful softmax and cross-entropy. Interviewers look for the vectorization mindset, correct broadcasting, and whether you stabilize the math before they have to ask. The skill is turning the math on the whiteboard into a few NumPy lines that would actually run on a batch.💻 Coding & Engineering CraftSign in
Core
Numerical Stability in CodeNumerical stability means writing arithmetic so floating-point error and overflow do not corrupt the result, which matters because naive ML math (softmax, cross-entropy, variance) quietly returns NaN or wrong gradients. Applied AI interviews test 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.💻 Coding & Engineering CraftSign in