Neural Networks
Recurrent neural network (RNN)
A recurrent neural network (RNN) is a type of neural network designed to process sequential data by maintaining a hidden state that captures information about past inputs. Unlike feedforward networks, RNNs have feedback connections, allowing them to use their internal memory to process arbitrary sequences of inputs.
Explanation
RNNs excel at tasks involving sequential data, such as time series analysis, natural language processing, and speech recognition. At each time step, the RNN receives an input and its previous hidden state. It then updates its hidden state based on both the input and the previous state, effectively creating a 'memory' of past inputs. This hidden state is then used to generate an output for that time step. A key challenge with traditional RNNs is the vanishing gradient problem, where gradients diminish exponentially as they are backpropagated through time, making it difficult to learn long-range dependencies. Variants like LSTMs and GRUs have been developed to address this issue. The architecture consists of input layer, hidden layer with recurrent connections, and output layer. The recurrent connection allows information to persist across time steps. The network is 'unrolled' in time during training, effectively creating a deep neural network where each layer represents a time step. This allows backpropagation through time (BPTT) to update the weights.