What are the differences between RNN and LSTM?

What are the differences between RNN and LSTM?

RNNs (Recurrent Neural Networks) are designed for sequential data but struggle with long-term dependencies. LSTMs (Long Short-Term Memory networks) are a type of RNN that can remember information for longer periods, making them better for tasks like language modeling.

Why do LSTMs perform better than RNNs?

LSTMs have a special architecture that includes gates to control the flow of information, allowing them to retain relevant information over longer sequences and avoid issues like vanishing gradients.

Can you give an example of where LSTMs are preferred over RNNs?

LSTMs are often preferred in applications like speech recognition, language translation, and time series prediction, where understanding context over long sequences is crucial.

Are there any downsides to using LSTMs?

Yes, LSTMs are more complex and computationally intensive than standard RNNs, which can lead to longer training times and require more resources.