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RNNs handle sequential data by using recurrent connections and a hidden state that carries information across time steps.
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 neural 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 a sequence. A key challenge for traditional RNNs is the vanishing gradient problem, which makes it difficult to learn long-range dependencies. Architectures such as long short-term memory (LSTM) and gated recurrent units (GRUs) were introduced to better preserve information over longer sequences. While transformers have become dominant for many sequence tasks due to self-attention and parallelization, RNNs remain important when computational efficiency, real-time processing, or inherently sequential data properties are critical. RNNs can also be configured in different ways—such as stacked, bidirectional, and encoder-decoder setups—to capture context from past (and sometimes future) elements more effectively.
RNNs handle sequential data by using recurrent connections and a hidden state that carries information across time steps.
The order of sequence elements is crucial because earlier outputs/hidden states affect later computations.
Traditional RNNs struggle with long-range dependencies due to vanishing gradients; LSTM/GRU address this with gating mechanisms.
RNNs can be structured in variants like stacked, bidirectional, and encoder-decoder models to capture different kinds of temporal context.
Data where meaning depends on the order of elements, such as words in a sentence or samples in a time series.
A neural network that processes sequences by feeding the previous hidden state (memory) into the next time step’s computation.
A memory vector in an RNN that is updated at each time step to summarize information from earlier inputs.
A relationship between elements of a sequence across different time steps that the model must learn.
A training issue where gradients become too small in deep/unrolled networks, limiting learning of long-range dependencies.
An RNN variant that uses gating to better retain and update information over long sequences.
An RNN variant similar to LSTM but with a simpler gating structure for efficient learning of sequence dependencies.
An RNN architecture that processes the sequence in both forward and backward directions to use past and future context.
A sequence transduction architecture that encodes an input sequence into internal representations and decodes them into an output sequence.
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