Our data science expert continues his exploration of neural network programming, explaining how regularization addresses the problem of model overfitting, caused by network overtraining. Neural ...

Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test accuracy is very low, the model highly overfits the training dataset set ...

This article is concerned with the computational aspect of l₁ regularization problems with a certain class of piecewise linear loss functions. The problem of computing the l₁ regularization path for a ...

In this paper we consider discrete inverse problems for which noise becomes negligible compared to data with increasing model norm. We introduce two novel definitions of regularization for ...

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 ...