How does an RNN work?

Can you explain how a Recurrent Neural Network (RNN) works?

Sure! An RNN is a type of neural network designed for processing sequences of data. Unlike traditional neural networks, RNNs have connections that loop back on themselves, allowing them to maintain a 'memory' of previous inputs.

What makes RNNs different from regular neural networks?

The key difference is that RNNs can handle sequential data by using their internal memory to remember information from previous time steps, which is crucial for tasks like language modeling or time series prediction.

Can you give me an example of where RNNs are used?

Absolutely! RNNs are commonly used in natural language processing tasks, such as language translation, text generation, and speech recognition.

What are some limitations of RNNs?

RNNs can struggle with long-range dependencies due to issues like vanishing gradients. This is why architectures like LSTMs and GRUs were developed to address these challenges.