recurrent neural network (RNN)

A recurrent neural network (RNN) is a neural network architecture for sequential data. This network applies the same recurrent transformation at each time step and passes a hidden state forward, allowing information to persist over time.

Classical forms include vanilla RNNs and gated variants like LSTM and GRU, which overcome vanishing and exploding-gradient problems via gating mechanisms during backpropagation through time.

Architectures can be unidirectional or bidirectional, and encoder–decoder setups (often augmented with attention) power many sequence-to-sequence tasks such as machine translation.

While transformer models now dominate many benchmarks, RNNs continue to be useful in streaming, low-latency, or resource-constrained environments.

Related Resources

Course

Building a Neural Network & Making Predictions With Python AI

In this step-by-step course, you'll build a neural network from scratch as an introduction to the world of artificial intelligence (AI) in Python. You'll learn how to train your neural network and make accurate predictions based on a given dataset.

intermediate data-science machine-learning