The Architecture of Memory: Design and Applications of Recurrent Neural Networks
The defining feature of an RNN design is the hidden state, often described as the network's "memory." Unlike a standard network that maps an input to an output , an RNN maps (input at time ht−1h sub t minus 1 end-sub (the previous hidden state) to a new hidden state Recurrent Neural Networks Design And Applications
Because RNNs excel at sequential data, their applications span across several critical domains: The Architecture of Memory: Design and Applications of
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: Recurrent Neural Networks Design And Applications
. This recursive process allows the network to build a representation of everything it has seen up to that point.