Exploring Optimization Dynamics: Hybrid Approaches Combining Adaptive and Traditional Techniques for Deep Learning Models

Optimizers are essential in deep learning to minimize error functions and ensure efficient learning. This paper evaluates the performance of prominent optimizers, including SGD, SGD with momentum, RMSprop, AdaGrad, Adam, and AdamW, across varied architectures and datasets. Tasks such as sentiment classification using LSTM, image classification using CNN, and reinforcement learning are examined based on convergence speed, accuracy, and hyperparameter robustness. The results reveal that while SGD exhibits consistent performance with momentum tuning, adaptive optimizers such as Adam achieve faster convergence, but may reduce generalization in specific datasets. A hyperparameter adjustment sensitivity analysis is conducted, offering practical guidelines for optimizer selection tailored to task requirements and computational constraints.