Regularization is a technique used to reduce the likelihood of neural network model overfitting. Model overfitting can occur when you train a neural network for too many iterations. This sometimes ...

The data science doctor continues his exploration of techniques used to reduce the likelihood of model overfitting, caused by training a neural network for too many iterations. Regularization is a ...

Understanding Regularization in Machine Learning Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function. This process can lead to ...

In this paper, we describe TRIPs-Py, a new Python package of linear discrete inverse problems solvers and test problems. The goal of the package is two-fold: 1) to provide tools for solving small and ...

[1] Toby Sanders, Rodrigo B. Platte, & Robert D. Skeel (2020). Effective new methods for automated parameter selection in regularized inverse problems. Applied Numerical Mathematics, 152, 29-48. [2] ...