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 ...

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 ...

This repository features a collection of Jupyter notebooks focused on regularization techniques and variable selection methods in Python. These notebooks highlight my expertise in improving model ...

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 ...

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 ...