The Architecture of Memory: Design and Applications of Recurrent Neural Networks
Traditional feed-forward neural networks operate on a fundamental limitation: they treat every input as independent of the last. This "amnesia" makes them unsuitable for tasks where context is king. Recurrent Neural Networks (RNNs) fundamentally changed this landscape by introducing loops into the network architecture, allowing information to persist. By maintaining an internal state, RNNs can process sequences of data, making them the primary architecture for anything involving time, order, or history. Architectural Design: The Feedback Loop 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. The Architecture of Memory: Design and Applications of
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 By maintaining an internal state, RNNs can process
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:
