Advancements in Deep Learning Architectures and Techniques for Enhanced Model Performance and Generalization

Abstract

Deep learning has revolutionized artificial intelligence by enabling complex pattern recognition and decision-making across various domains. This paper explores the latest advancements in deep learning architectures and optimization techniques that contribute to enhanced model performance and generalization. We analyze improvements in convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, attention mechanisms, and novel training paradigms. Additionally, this paper presents a comparative analysis of emerging techniques, their impact on computational efficiency, and generalization across datasets. Through experimental evaluations and theoretical discussions, we provide insights into how deep learning continues to evolve towards more robust, interpretable, and efficient models.