Implement a neural network framework that supports advanced optimization techniques including adaptive learning rate methods (Adam, RMSProp, Adagrad), regularization techniques (dropout, batch normalization, L1/L2 regularization), and specialized architectures (residual connections, attention mechanisms). Provide benchmarks comparing these techniques on standard datasets, with detailed analysis of convergence behavior, performance characteristics, and practical recommendations for different problem domains.