This article presents a theoretical framework explaining why deep neural networks generalize well despite being overparameterized—a phenomenon that violates classical machine learning theory. The proposed theory, based on analyzing neural networks as dynamical systems in output space rather than parameter space, uses the empirical Neural Tangent Kernel to explain how gradient descent implicitly selects solutions that generalize, offering IT leaders a more rigorous foundation for understanding and operationalizing AI/ML systems at scale. This breakthrough in deep learning theory has direct implications for enterprise AI strategy, model validation, and resource allocation decisions that CIOs must make when deploying neural network-based solutions.
This article presents a theoretical framework explaining why deep neural networks generalize well despite being overparameterized—a phenomenon that violates classical machine learning theory. The proposed theory, based on analyzing neural networks as dynamical systems in output space rather than parameter space, uses the empirical Neural Tangent Kernel to explain how gradient descent implicitly selects solutions that generalize, offering IT leaders a more rigorous foundation for understanding and operationalizing AI/ML systems at scale. This breakthrough in deep learning theory has direct implications for enterprise AI strategy, model validation, and resource allocation decisions that CIOs must make when deploying neural network-based solutions.
