Why do traditional RNNs face difficulties in learning long-term dependencies?
- Vanishing Gradient Problem
- Overfitting
- Underfitting
- Activation Function Selection
Traditional RNNs face difficulties due to the "Vanishing Gradient Problem." During backpropagation, gradients can become extremely small, making it challenging to update weights for long sequences. This issue inhibits the model's ability to learn long-term dependencies effectively, a critical limitation in sequence data tasks.
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