What is Batch Size?

The number of training examples processed in one forward/backward pass before updating weights. Larger batches give more stable gradient estimates but use more memory. Typical values: 32-512 for training, larger for inference. Batch size interacts with learning rate -- double the batch, double the LR (linear scaling rule).

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How many examples at once

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