Recurrent Neural Networks (RNNs) are a type of artificial intelligence model that’s designed for handling sequential data, or data where the order matters.
An easy way to understand RNNs is to think about them as a reader with a good memory. Just like when you read a sentence, you remember the previous words to understand the current word, an RNN does the same with data. It remembers the previous data points to understand the current one.
In a more technical sense, RNNs have loops that allow information to be passed from one step in the sequence to the next. This gives RNNs a kind of memory. They can use this memory to process sequences of data, like a time series or the words in a sentence.
However, traditional RNNs have a problem known as the “vanishing gradients” problem, which makes it hard for them to learn and remember information from long sequences. To address this, researchers have developed more advanced types of RNNs, such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) networks, which have mechanisms to remember information over longer periods.
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