What are the differences between LSTMs and Transformers?

What are the differences between LSTMs and Transformers?

LSTMs (Long Short-Term Memory networks) are a type of recurrent neural network designed to remember information for long periods. Transformers, on the other hand, use self-attention mechanisms to process data in parallel, making them more efficient for tasks like language translation.

Which one is better for natural language processing?

Transformers are generally considered better for natural language processing tasks due to their ability to handle long-range dependencies and parallelize training.

Can you give examples of applications for both?

Sure! LSTMs are often used in time series prediction and speech recognition, while Transformers are widely used in machine translation and text summarization.

What are the limitations of each?

LSTMs can struggle with very long sequences and are slower to train, while Transformers require a lot of data and computational power.

Are there any hybrid models that use both?

Yes, some models combine LSTMs and Transformers to leverage the strengths of both architectures, especially in complex tasks.