Three Crazily Simple Recipes to Fight Overfitting in Deep Learning Models

Overfitting is considered one of the biggest challenges in modern deep learning applications. Conceptually, overfitting occurs when a model generates a hypothesis that is too tailored to a specific dataset to the data making it impossible to adapt to new datasets. A useful analogy to understand overfitting is to think about it as hallucinations in the model. A lot has been written about overfitting singe the early days of machine learning so I won't presume to have any clever ways to explain it. However, I would like to use this post to present three practical ways to think about overfitting in deep learning models.