Recurrent neural networks (RNNs) are designed to handle sequences of data, such as time series or natural language. RNNs have connections between neurons that form directed cycles, allowing them to maintain an internal state that can capture information from previous time steps. This enables RNNs to process input data with temporal dependencies. However, RNNs can suffer from vanishing or exploding gradients, which can hinder learning in long sequences. Techniques like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have been developed to address these issues.
Updated 5 months ago