Regularization and Generalization in Deep Learning
About Regularization and Generalization Regularization is one of the most important concepts in machine learning. In mathematics and statistics, finance, computer science, machine learning and inverse problems regularization is the process of adding information to solve an ill-posed problem. In the context of machine learning optimization problems, it applies a modification to the objective functions to reduce generalization error of the learning model even at the cost of increased training error. Generalization refers to the capability of a trained model to make the right predictions when faced with unknown input data during its operational life. In the rest of this article, we try to gain an intuitive understanding of the mathematical basis of regularization theory for inverse problems and its application to improve the generalization performance of learning algorithms. I have included some mathematical content to emphasize that, there is sufficiently strong mathematical founda