Dropout: When forgetting is better
Overfitting. The bane of every machine learning engineer’s existence. You train your model, it performs amazingly on training data, and then… it completely fails on real-world data. Sound familiar? I remember the first time I encountered this problem. I was working on an image classification task, my model was getting 99% accuracy on training data but only 96% on validation. At first glance, it seemed fine - but that 3% gap was a red flag. The model was starting to memorize specific training examples instead of learning generalizable patterns. ...