Ernie Smith is a former contributor to BizTech, an old-school blogger who specializes in side projects, and a tech history nut who researches vintage operating systems for fun. In data analysis, it is ...

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 ...

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 ...

Clear, visual explanation of the bias-variance tradeoff and how to find the sweet spot in your models. #BiasVariance #Overfitting #MachineLearningBasics Mexico's Sheinbaum blasts Trump admin's move: ...

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 ...