What are the limitations of Recurrent Neural Networks (RNNs)?

What are the limitations of Recurrent Neural Networks (RNNs)?

RNNs have several limitations, including difficulty in learning long-term dependencies, vanishing gradients, and computational inefficiency.

Can you explain the vanishing gradient problem?

The vanishing gradient problem occurs when gradients become too small for effective learning, making it hard for RNNs to learn from long sequences.

Are there alternatives to RNNs that address these issues?

Yes, alternatives like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) are designed to overcome these limitations.

What are LSTMs and how do they work?

LSTMs are a type of RNN that use special gates to control the flow of information, allowing them to remember long-term dependencies better.

How do GRUs differ from LSTMs?

GRUs are a simplified version of LSTMs, combining the forget and input gates into a single update gate, making them faster and easier to train.