. This recursive process allows the network to build a representation of everything it has seen up to that point.
Because RNNs excel at sequential data, their applications span across several critical domains:
However, basic RNNs suffer from the "vanishing gradient problem," where information from earlier steps fades away during training. This led to the design of more sophisticated cells:
