As we know, overfitting is a major challenge that machine learning developers face. It becomes a big challenge when the neural network architecture is complex and training data is huge. While mentioning overfitting, we're not ignoring the chances of underfitting at all. We will keep overfitting and underfitting in the same category. Let's discuss how we can combat overfitting problems.

The following are possible reasons for overfitting, including but not limited to:

• Too many feature variables compared to the number of data records
• A complex neural network model

Self-evidently, overfitting reduces the generalization power of the network and the network will fit noise instead of a signal when this happens. In this recipe, we will walk through key steps to prevent overfitting problems.