Comparative Analysis of Recurrent Neural NetworkUsing Various Optimizers and Datasets

Recurrent Neural Networks (RNNs) have emerged as a paradigm-defining architecture for temporal pattern recognition, demonstrating exceptional proficiency in modeling sequential dependencies inherent to natural language processing (NLP) and multivariate time-series analysis. This rigorous comparative investigation systematically evaluates the triumvirate of recurrent architectures— naïve RNNs, Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU)— under four distinct optimization regimes: Nesterov- accelerated Adaptive Moment Estimation (Nadam), Adaptive Moment Estimation (Adam), Root Mean Square Propagation (RMSprop), and Stochastic Gradient Descent (SGD). Through empirical examination across two linguistically disparate corpora— the IMDB dataset (50,000 binary sentiment-labeled reviews) and Reuters newswire corpus (11,228 articles across 46 multi-class topical categories)— we establish that GRU architectures paired with Nadam optimization achieve state-transitional superiority, attaining peak validation accuracies of 88.15% (IMDB) and 78.30% (Reuters). Notably, GRUs demonstrate 20-25% faster convergence than LSTMs (9 vs. 10 epochs on IMDB; 12 vs. 13 epochs on Reuters) while maintaining parameter efficiency through streamlined gating mechanisms. The computational topology reveals GRUs require merely 289s/epoch (IMDB) and 260s/epoch (Reuters)— an 18% reduction compared to LSTMs— without compromising representational capacity. Our tripartite analysis dissects performance matrices through: