What are the advantages of using RNNs in machine learning?

What are the advantages of using RNNs in machine learning?

RNNs, or Recurrent Neural Networks, are great for sequential data. They can remember previous inputs, making them ideal for tasks like language modeling and time series prediction.

Can you explain how they remember previous inputs?

RNNs use hidden states to maintain information from previous time steps. This allows them to capture dependencies in sequences, which is crucial for understanding context.

What types of problems are RNNs particularly good at solving?

RNNs excel in tasks like natural language processing, speech recognition, and any application involving sequential data, such as stock price prediction.

Are there any limitations to using RNNs?

Yes, RNNs can struggle with long-term dependencies and are often slower to train compared to other architectures like CNNs or Transformers.