Batch Processing

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Batch processing is a way to train AI model using small chunks of data instead of trying to use the entire dataset at once. The model looks at a batch, measures how wrong its predictions are, updates its parameters, and then moves on to the next batch. A simple way to picture it is studying with flashcards in small stacks. You learn a bit, adjust, then take the next stack, rather than trying to review every card in one go.

This approach is practical because modern hardware can process batches efficiently, which speeds up training and keeps updates more stable than learning from one example at a time. Data is usually shuffled each time the model goes through the dataset so the model doesn’t accidentally learn from the order of examples.

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