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 ...

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 ...

We study the consistency of the estimator in a spatial regression with partial differential equation (PDE) regularization. This new smoothing technique allows us to accurately estimate spatial fields ...