The 60-second answer
SGD uses a shared learning-rate schedule and momentum; Adam adapts updates using first- and second-moment estimates per parameter. Adam often converges faster and is forgiving early in training; well-tuned SGD can generalize strongly in some vision/classical deep-learning settings.
Build the answer in this order
SGD uses a shared learning-rate schedule and momentum; Adam adapts updates using first- and second-moment estimates per parameter.
Adam often converges faster and is forgiving early in training; well-tuned SGD can generalize strongly in some vision/classical deep-learning settings.
Compare them with the same schedule, batch size, regularization, and training budget rather than from default hyperparameters.
Mention AdamW for decoupled weight decay and monitor optimization stability, validation quality, and total compute-to-quality.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
Senior-level signal
- Senior answers distinguish optimizer convergence speed from final generalization and total system cost.
- Discuss large-batch scaling, warmup, gradient clipping, and numerical precision when training at scale.
What the interviewer is really testing
Likely follow-up questions
Common weak-answer patterns
- Reciting a definition without mechanism or assumptions.
- Claiming one technique is always better without a data regime.
- Stopping before failure modes, validation, or deployment implications.