Introducing RNNs
RNNs, are a special breed of neural networks designed specifically for sequential data. Here’s a breakdown of their key attributes.
The term “recurrent” stems from the unique feedback loop RNNs possess. Unlike traditional neural networks, which are essentially stateless and produce outputs solely based on the current inputs, RNNs carry forward a “state” from one step in the sequence to the next.
When we talk about a “run” in the context of RNNs, we’re referring to a single pass or processing of an element in the sequence. So, as the RNN processes each element, or each “run,” it retains some information from the previous steps.
The magic of RNNs lies in their ability to maintain a memory of previous runs or steps. They achieve this by incorporating an additional input, which is essentially the state or memory from the previous run. This mechanism allows RNNs to recognize and learn the...
