Recurrent Neural Network

Imagine you're reading a book. You don't just read each word in isolation, you remember previous words to understand the current sentence. RNNs work in a similar way. They have a built-in 'memory' that allows information to persist and be used across different steps in a sequence. This makes them ideal for tasks where the order of information matters a lot.

For example, if you're trying to predict the next word in a sentence, an RNN can consider the words that came before it to make a more accurate guess. This 'recurrent' connection, where the output from one step feeds back into the input for the next, is what gives RNNs their powerful ability to learn from sequential data.

While very effective, standard RNNs can sometimes struggle with remembering information over very long sequences. This challenge led to the development of more advanced versions, like LSTMs and GRUs, which are specialized types of RNNs designed to better handle long-term dependencies. Despite these advancements, the core concept of the RNN remains a fundamental building block in understanding how AI processes sequential information.