RNNs · Level 3 of 5
Backpropagation Through Time
Differentiating a recurrent computation by unrolling its sequence steps.
Shared parameters receive contributions from multiple time positions.
Example
A sequence loss propagates through successive hidden-state updates.
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Audio transcript
Backpropagation Through Time. Differentiating a recurrent computation by unrolling its sequence steps. Shared parameters receive contributions from multiple time positions. For example: A sequence loss propagates through successive hidden-state updates.
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Why it matters
This helps you trace information and gradient flow through sequence models.
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Quick recall question
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