Apply the chain rule to a recurrent network unrolled over its input sequence, adding contributions to parameters reused at multiple time steps. Products of state Jacobians convey effects from earlier states to later losses. This can compute exact gradients for the chosen finite unrolling, while physiological availability of the same history and error signals is a separate question.

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  1. Backpropagation
  2. Feedforward neural network
  3. Neural network
  4. Statistical learning
  5. Statistical modelling
  6. Statistical model
  7. Probability and statistics
  8. Area of mathematics
  9. Mathematics
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