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RNNs process sequences by maintaining and updating a hidden state, enabling learning of temporal dependencies.
Recurrent neural networks (RNNs) are designed to process sequential data—such as text, speech, and time series—where the order of elements matters. Unlike feedforward networks that treat inputs independently, RNNs use recurrent connections so that information from earlier time steps influences later processing. This is achieved through a hidden state (a memory vector) that is updated at each step based on the current input and the previous hidden state, allowing the model to learn temporal dependencies and patterns across time. However, standard RNNs struggle with learning long-range relationships due to the vanishing gradient problem. Architectures such as long short-term memory (LSTM) and gated recurrent units (GRUs) were developed to better preserve information over longer sequences. More recently, transformer models have become dominant for many sequence tasks because they handle long-range dependencies effectively and allow greater parallelization, but RNNs remain important when computational efficiency, real-time processing, or inherently sequential data is required. RNNs can also be organized into variants like stacked RNNs (multiple recurrent layers), bidirectional RNNs (processing both past and future context), and encoder-decoder setups (common in sequence transduction tasks like machine translation).
RNNs process sequences by maintaining and updating a hidden state, enabling learning of temporal dependencies.
Standard RNNs often fail to capture long-range dependencies well because of the vanishing gradient problem.
LSTM and GRU architectures were introduced to mitigate long-range learning issues; bidirectional and encoder-decoder variants extend RNN capabilities.
Transformers have largely replaced RNNs in many NLP tasks due to better long-range dependency handling and parallelizability, but RNNs remain useful for efficiency and real-time sequential modeling.
Data where the order of elements (e.g., time steps in a time series or tokens in text) carries meaning for the task.
A relationship where information from earlier time steps helps determine outputs at later time steps.
A network connection that feeds information from one time step back into the network for the next time step.
A vector in an RNN that summarizes information from previous inputs and is updated at each time step.
A training issue where gradients become very small in deep/unrolled recurrent computations, limiting learning of long-range dependencies.
An RNN architecture that uses gating mechanisms to better retain and update information over long sequences.
A computationally efficient gated RNN variant that helps capture longer dependencies than basic RNNs.
An RNN variant that processes the sequence in both forward and backward directions to use past and future context.
A sequence transduction architecture that uses an encoder RNN to summarize an input sequence and a decoder RNN to generate an output sequence.
The process of expanding an RNN across time steps so recurrent computation can be viewed as a deep network for training.
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