RNNs: Neural Networks with Short-Term Memory
A Recurrent Neural Network (RNN) processes sequences by keeping a running memory of what it's seen. It feeds its own output from one step back into the next, like someone reading a sentence one word at a time. This is ideal for sequential data like text or time series where context is key. The main footgun is its notoriously short memory; information from early in a long sequence often gets lost.
Think of a Recurrent Neural Network (RNN) as a neural net that reads a sequence one item at a time, keeping a running 'summary' of what it has seen so far. Unlike networks that process data in one go, an RNN has a feedback loop, feeding its output from the previous step back into the current one. This 'memory' is crucial for sequential data where order matters, like in speech recognition or predicting the next word in a sentence. The critical footgun is that this memory is very short-lived. As a sequence gets longer, the network struggles to retain information from the beginning, a problem called the vanishing gradient, which led to more advanced architectures.
Read the original → Wikipedia: Recurrent neural network
- #rnn
- #neural networks
- #sequential data
- #generative ai
Get five bites like this every day.
Tezvyn delivers a daily feed of 60-second tech bites with quizzes to lock in what you learn.