Batch size
The number of training examples processed together before a single weight update. A larger batch produces a more stable gradient estimate at the cost of more memory per step, a smaller batch updates more often on noisier estimates.
Why exams ask this
Tested as a memory-versus-stability tradeoff. A larger batch size gives a smoother, more accurate gradient estimate but costs more memory per step, and the exam wants that tradeoff named, not "bigger is always better."
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