RNN stands for Recurrent Neural Network, a class of artificial neural networks designed to recognize patterns in sequences of data such as text, time series, audio, or video. Unlike traditional feedforward neural networks, RNNs have loops that allow information to persist over time. This means RNNs can take previous outputs or hidden states as part of their input for current predictions, making them especially well-suited to sequential tasks where context and order matter.
The core idea behind RNNs is their ability to process inputs of arbitrary length by maintaining an internal state that gets updated as each new data point is processed. This internal state, often called the hidden state, “remembers” information about earlier parts of the sequence. For example, in natural language processing, an RNN can keep track of previous words in a sentence to help predict the next word.
A simple RNN processes data one element at a time while maintaining the hidden state across each step. At each timestep, the network takes the current input and the previous hidden state, combines them, and computes the next hidden state and output. This design allows RNNs to model dependencies that span across time or position in a sequence.
However, basic RNNs have some challenges. One well-known issue is the vanishing gradient problem, where the network struggles to learn long-range dependencies because gradients used in training shrink as they are propagated back through many timesteps. This makes it difficult for RNNs to retain information across long sequences. To address this, more advanced RNN architectures like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) introduce gating mechanisms that help preserve and manage information over longer periods.
RNNs have been widely used for tasks such as language modeling, machine translation, speech recognition, and time series prediction. For example, in language modeling, an RNN can predict the next word in a sentence based on the context provided by preceding words. In speech recognition, it can process audio signals over time to transcribe spoken words.
Training RNNs typically involves a method called backpropagation through time (BPTT), which extends standard backpropagation to handle the sequential nature of the data. Like other neural networks, RNNs use parameters that are updated based on the error between predictions and actual outputs, often with optimization algorithms such as gradient descent.
While RNNs are powerful for modeling sequences, they have been largely supplemented in recent years by architectures like Transformers, which can handle longer-range dependencies more efficiently and in parallel. Nonetheless, understanding RNNs is essential for grasping the evolution of AI models designed for sequential data.
