Deep learning models, with their vast capacity to fit complex data patterns, are prone to overfitting when trained on limited or noisy datasets. Regularization techniques act as constraints or ...

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 ...