Walle
Created page with "{{see also|Machine learning terms}} ==Dropout Regularization in Machine Learning== Dropout regularization is a technique used in machine learning to prevent overfitting in neural networks. Overfitting occurs when a model learns to perform well on the training data but fails to generalize to unseen data. This article discusses the concept of dropout regularization, its implementation, and its advantages in the context of neural networks. ===Concept=== Dropout regularizat..."
