Understanding Recurrent Neural Networks (RNNs)

Recurrent neural networks (RNNs) are a type of neural network that is designed to handle sequential data. They have a feedback loop that allows information from previous time steps to influence the current step, which is useful for modeling temporal relationships in data.

In an RNN, the hidden state (the internal representation of the network) is allowed to persist across time steps, so that information from previous steps can be used to inform the current step. This makes RNNs well-suited for tasks such as language modeling, where the network needs to keep track of the context of a sentence over multiple words.

Recurrent neural networks are designed to handle sequential data and have a feedback loop that allows information from previous time steps to influence the current step.