Answer / Rinshu Kanaujiya

Training recurrent neural networks (RNNs) can be challenging due to several reasons:

1. Vanishing or exploding gradients: Gradient backpropagation through many time steps in RNNs can lead to either vanishing or exploding gradient problems, making the training process unstable and slow.
2. Long-term dependencies: RNNs are designed to handle sequences of varying lengths but struggle with capturing long-term dependencies in data. This issue is often addressed through techniques such as LSTM (Long Short-Term Memory) networks or GRU (Gated Recurrent Units).
3. Vanishing gradients during backpropagation: The vanishing gradient problem, which arises due to the multiplicative nature of RNNs and their chain rule implementation, can make it difficult for the network to learn patterns that are far apart in time steps.

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