What are the limitations of LSTM?

What are the limitations of LSTM networks?

LSTM networks, while powerful, have limitations such as difficulty in learning long-term dependencies, high computational cost, and susceptibility to overfitting.

Can you explain why they struggle with long-term dependencies?

LSTMs use gates to control information flow, but in practice, they can still forget important information over long sequences.

Are there better alternatives to LSTMs?

Yes, alternatives like Transformers have gained popularity due to their ability to handle long-range dependencies more effectively.

What are the computational costs associated with LSTMs?

LSTMs require more parameters and complex operations, leading to longer training times and higher resource consumption compared to simpler models.

How do LSTMs compare to GRUs?

GRUs are a simpler variant of LSTMs with fewer parameters, which can lead to faster training times while still performing well on many tasks.