How do GRU and LSTM compare in performance?

How do GRU and LSTM compare in performance?

GRU and LSTM are both types of recurrent neural networks. Generally, GRUs are faster and require less memory, while LSTMs can capture more complex patterns due to their architecture.

In what scenarios would I prefer using GRU over LSTM?

You might prefer GRU when you have limited data or computational resources, as they are simpler and often perform comparably to LSTMs in many tasks.

Are there specific tasks where LSTM outperforms GRU?

Yes, LSTMs can outperform GRUs in tasks that require learning long-term dependencies, such as language modeling or complex sequence prediction.

What are the main differences in their architectures?

The main difference is that LSTMs have three gates (input, output, forget) and a cell state, while GRUs have two gates (reset and update) and combine the cell state with hidden state.