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A model trains on one batch of data samples at a time, until all samples are used up. (For example, for 5000 samples, a batch size of 100 requires running 50 batches.) The model improves after each batch is processed. You must find a balance between a large batch size, which allows more significant improvement after each run, and a large number of batches, which allows for numerous opportunities to improve.

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