Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are a type of neural network that are particularly useful for sequential data, such as time series or natural language. They are designed to process data with a temporal component, by maintaining a state that captures information from previous inputs.

History:

• RNNs were first introduced in the 1980s by Sepp Hochreiter and Jürgen Schmidhuber.

• They were initially developed for speech recognition tasks, but have since been applied to a wide range of sequential data tasks, including natural language processing, music generation, and machine translation.