Recurrent Architectures: LSTMs, GRUs, and Sequential Learning Systems

Time-series and sequential data streams contain temporal dependencies. Standard recurrent networks suffer from vanishing gradients over long horizons. Long Short-Term Memory (LSTM) cells and Gated Recurrent Units (GRUs) resolve this using memory cell states and input, output, and forget gates. We analyze memory propagation and comparative latency benchmarks.