What are the limitations of LSTM networks?

What are the limitations of LSTM networks?

LSTM networks, while powerful, have several limitations including high computational cost, difficulty in parallelization, and challenges with long-term dependencies.

Can you explain why they have difficulty with long-term dependencies?

LSTMs are designed to remember information for long periods, but in practice, they can still struggle with very long sequences due to vanishing gradients.

Are there any alternatives to LSTMs that address these issues?

Yes, alternatives like Transformers and GRUs (Gated Recurrent Units) can handle long-range dependencies more effectively and are often more efficient.

What are the advantages of using Transformers over LSTMs?

Transformers allow for parallel processing of data, which speeds up training, and they use self-attention mechanisms to capture relationships in data more effectively.