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Overfitting in machine learning can take on many different forms. Most commonly, it happens when an AI model learns too much of the noise in the data, and fails to make any useful generalizations. This leads to dismal performance when we assess the model on data it has not seen before. There are many techniques that have been developed to mitigate overfitting, but such methods can often prove challenging to implement, especially when you are just getting started on your journey. However, a recent paper, published by a group of diligent Harvard Alumni, suggests that on certain tasks, overfitting may be a problem of the past. This article will walk you through the research paper, and demonstrate how you can build world-class AI models, inline with the world's leading research.

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