A common method to combat the vanishing gradient problem in RNNs is to use _______.
- Long Short-Term Memory (LSTM)
- Decision Trees
- K-Means Clustering
- Principal Component Analysis
To address the vanishing gradient problem in RNNs, one common technique is to use Long Short-Term Memory (LSTM) networks. LSTMs are a type of RNN that helps mitigate the vanishing gradient problem by preserving and updating information over long sequences. LSTMs are designed to capture long-term dependencies and are more effective than traditional RNNs for tasks where data from distant time steps is important.
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