Artificial Intelligence & Machine Learning
Recurrent Neural Network (RNN)
Definition
A Recurrent Neural Network (RNN) is a type of artificial neural network which uses sequential data or time series data. They have an internal "memory" that allows them to persist information from previous inputs in a sequence to influence the current output.
Why It Matters
RNNs were designed to handle sequential data, making them suitable for tasks like natural language processing, speech recognition, and time series forecasting.
Contextual Example
When processing a sentence, an RNN would process it one word at a time. The output for the word "happy" in the sentence "The cat was happy" would be influenced by the information from the previous words "The" and "cat".
Common Misunderstandings
- Standard RNNs suffer from the "vanishing gradient problem," which makes it hard for them to learn long-range dependencies.
- More advanced architectures like LSTMs and GRUs were developed to solve this problem and have largely replaced simple RNNs.