Recurrent neural network
Recurrent neural network
(also RNN)
Recurrent neural network definition
A recurrent neural network is a special class of artificial neural networks designed to work with sequential data. Recurrent neural networks process sequences of input data one element at a time, using outputs from previous elements to build an information base for subsequent inputs.
See also: artificial intelligence, autonomous intelligence, augmented intelligence, machine learning
Real uses for recurrent neural networks
- Natural language processing: This includes tasks like language modeling, machine translation, sentiment analysis, and speech recognition.
- Time series analysis: Recurrent neural networks can efficiently analyze financial, weather, sensor, and other time series data.
- Image and video processing: Recurrent neural networks are used in sequential processing tasks like action recognition, video captioning, and image captioning.
- Generative models: Recurrent neural networks can be “trained” on specific datasets to generate new data sequences (such as music, text, or images) based on the dataset’s patterns.
Types of recurrent neural networks
- Vanilla: Vanilla networks have a single layer of recurrent units that connect to each other in a chain. In addition to any external input, each unit also considers its own output from previous timesteps when processing requests. This is the simplest type of recurrent neural network architecture.
- Long short-term memory (LSTM): LSTM networks were developed to address the vanishing gradient problem of vanilla networks (as time goes on, the gradients used to update the weights for information shrink exponentially, preventing the network from learning any further). Thanks to additional memory cells and gating mechanisms, LSTM networks can selectively remember or forget information from previous timesteps.
- Gated recurrent unit (GRU): Similar to LSTM networks, GRU networks were developed to address the vanishing gradient problem of vanilla RNNs. GRU networks have fewer parameters than LSTM networks and boast higher computation speed, but they also perform worse at tasks that require longer-term memory.