A Recurrent Neural Network (RNN) is a type of neural network architecture designed to process sequential data by maintaining an internal state or memory. RNNs allow information to be propagated in a loop, allowing the network to retain information about past inputs. RNNs are well-suited for tasks where order and context matter, such as natural language processing (NLP), speech recognition, time series prediction, and handwriting recognition.
Key features…
Recurrent Connections: RNNs have loops, allowing information from previous time steps to be passed on to the current time step. This allows the network to maintain memory of past inputs.
Hidden State or Memory: RNNs have an internal hidden state or memory, which is updated at each time step. The hidden state encodes information about the context and history of the sequence thus far.
Problem of Vanishing and Exploding Gradient: Gradients can become extremely small or large during backpropagation through time, which can lead to difficulties in learning long-range dependencies.
Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU): To address the vanishing gradient problem and better capture long-term dependencies, variants of RNNs such as LSTM and GRU have been introduced. These models have specialized gating mechanisms that help control the flow of information in the network.
Sequence-to-Sequence Models: RNNs are commonly used in sequence-to-sequence models, where the input sequence is transformed into another sequence of variable length. Such models are widely used in machine translation, text generation, and speech synthesis. s