Modern Neural Network

Modern neural networks are complex computational models designed to learn intricate patterns from data, primarily aiming to improve accuracy and efficiency in various tasks. Current research focuses on optimizing these networks for specific hardware (e.g., using CPUs and specialized architectures like CGAs), improving training efficiency through techniques like structured pruning and Bayesian methods, and enhancing robustness to small data transformations. These advancements are crucial for deploying neural networks in resource-constrained environments and for building more reliable and trustworthy AI systems across diverse scientific and engineering applications.