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
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
A streamlined version of the LSTM that merges gates for efficiency while maintaining similar performance. Diverse Applications The defining feature of an RNN design is