Tail-chasing memory keeper
A Recurrent Neural Network (RNN) is a type of artificial neural network that uses loops to remember previous inputs, making it ideal for processing sequences like sentences or stock prices.
Imagine you're reading a sentence one word at a time, but you keep a sticky note of the last few words so you know what's happening. An RNN works like that: it has a tiny memory that passes along information from one step to the next, so it can understand the flow of time or order.
RNNs power many everyday tools like voice assistants (Siri, Alexa) and Google Translate, because they can handle sequences like speech or text. They matter because they were the first neural networks to effectively model time-dependent data, paving the way for modern language models.
Many think RNNs can remember information forever, but they actually suffer from 'vanishing gradients' and forget long-term dependencies. They're better at short-term memory—like remembering the last few words—not entire paragraphs.
A Recurrent Neural Network is a class of neural networks where connections between nodes form a directed cycle, allowing information to persist across time steps. It processes sequential data by maintaining a hidden state that is updated at each step based on the current input and previous hidden state. Training uses backpropagation through time (BPTT), which can be computationally expensive and prone to gradient issues.