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A recurrent neural network (RNN) is a neural network that processes a sequence by updating an internal state as each input arrives. Because each update can use the previous state, information from earlier steps can influence how the network handles later ones.
What makes a neural network recurrent?
A feed-forward network processes an input without carrying a recurrent state from one sequence step to the next. An RNN instead maintains a hidden state: a compact representation that is updated as the network reads each new item. PyTorch summarizes the idea as “a network that maintains some kind of state” in its sequence-model tutorial.
The state carries information forward, but it should not be understood as a perfect record of everything the network has seen. It is a learned representation of context that can affect later processing.
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A basic recurrent update is written as h_t = f_W(h_{t-1}, x_t). Here, x_t is the input at the current step, h_{t-1} is the prior hidden state, and h_t is the updated state. The function f_W uses learned parameters, denoted by W.
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For a simple vanilla RNN, Stanford’s CS231n notes show the update as h_t = tanh(W_hh h_{t-1} + W_xh x_t). An output can then be computed from the hidden state. The important idea is that the same learned transition is applied at each step, rather than using a separate transition for every sequence position. That lets the model handle sequences of different lengths.
What kinds of tasks can RNNs handle?
RNNs can be arranged to consume a sequence, produce a sequence, or do both. The right arrangement depends on the task, not on a single required input-output format.
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- Language modeling: process text step by step to model or generate a word sequence.
- Image captioning: use an image representation as input and generate a sequence of words.
- Sequence-to-sequence tasks: map an input sequence to an output sequence.
Stanford’s RNN material describes these sequence arrangements, and its Spring 2026 course schedule lists RNN, LSTM, and GRU alongside language modeling, image captioning, and sequence-to-sequence.
How is a vanilla RNN different from an LSTM or GRU?
“RNN” can mean the broader family of recurrent networks. A vanilla RNN, also called an Elman RNN, is a simpler form with a basic hidden-state recurrence. LSTM and GRU are gated recurrent variants: their mechanisms regulate how information flows through the state.
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Vanilla RNNs can struggle to learn dependencies separated by many time steps. During training, gradients propagated through a long sequence may vanish or grow excessively, making distant context difficult to learn. An LSTM’s cell-state mechanism can make long-distance information easier to preserve, but it does not guarantee that gradient problems disappear. The CS231n discussion of recurrent networks explains this challenge.
There is no universal winner among these variants. The useful comparison depends on the task, the sequence arrangement, and how much long-range information the model needs to learn.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does an RNN layer do in PyTorch?
PyTorch’s documented RNN layer is a concrete implementation of an Elman-style recurrence. Each layer combines the current input and prior hidden state using learned weights and biases, then applies tanh by default; it can use ReLU when configured. Those details describe that framework’s layer, not every architecture that may be called an RNN.
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