Definition of Recurrent Neural Network (RNN)
A type of artificial neural network designed to recognize patterns in sequences of data, such as time series or text.
Explanation of Recurrent Neural Network (RNN)
A Recurrent Neural Network (RNN) is a type of artificial neural network designed for processing sequences of data. Unlike traditional neural networks, RNNs have connections that form directed cycles, allowing them to maintain a memory of previous inputs and capture temporal dependencies. This makes RNNs particularly well-suited for tasks involving sequential data, such as time series forecasting, natural language processing, and speech recognition. RNNs process data one step at a time, maintaining a hidden state that is updated at each step based on the current input and the previous hidden state. This allows them to learn patterns and dependencies in the data over time. Training RNNs involves techniques such as backpropagation through time (BPTT) to adjust the network’s weights and improve its performance. Despite their capabilities, RNNs can suffer from issues like vanishing gradients, which can limit their effectiveness in capturing long-term dependencies. Variants like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have been developed to address these challenges and improve the performance of RNNs.