Recurrent Neural Networks (RNNs)

The Simple Explanation

Imagine you're trying to understand a sentence. You can't just look at each word individually; the order is critical! A standard neural network is like someone with no short-term memory—it looks at one piece of information (like a single word) and then immediately forgets it when it sees the next one.

Recurrent Neural Networks (RNNs) are a special type of network built to handle sequences of data, like sentences, stock prices over time, or musical notes. Their superpower is memory.

Here’s how it works:

• An RNN processes data one step at a time (e.g., one word in a sentence).
• As it processes an item, it doesn't just produce an output; it also updates its internal memory (technically called a hidden state).
• This memory is then passed along to the very next step. So, when the network looks at the next word, it has the context of all the words that came before it.

This internal loop allows information to persist, or be 'remembered', making RNNs perfect for tasks where context and order are crucial.