Recurrent Neural Networks (RNNs) are a type of neural network that are specifically designed to handle sequential data, where the output at a given time step depends not only on the current input, but also on the input and output of previous time steps.
RNNs can be thought of as a network of nodes, each of which has a "memory" or "hidden state" that is updated at each time step based on the current input and the previous hidden state. This hidden state can then be used to generate an output at that time step, which can be fed back into the network as input for the next time step.
The basic structure of an RNN involves a single "recurrent" layer of nodes, where each node is connected to every other node in the same layer. This allows the network to learn relationships between elements of the sequence that are far apart in time.
RNNs are commonly used in a wide range of applications, including natural language processing, speech recognition, and time series prediction. One of the key advantages of RNNs is their ability to handle variable-length input sequences, making them well-suited for tasks where the length of the input varies, such as text processing. However, one of the challenges with RNNs is the problem of vanishing gradients, which can occur when the gradients used to update the weights become very small over time, making it difficult for the network to learn long-term dependencies. This issue has been addressed with the development of variants such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) networks, which use more sophisticated gating mechanisms to allow the network to selectively remember or forget information at each time step.