AI/ML Dropout Regularization to Handle Overfitting in Deep Learning Models

Dropout regularization is a technique that randomly drops a number of neurons in a neural network during model training

This means the contribution of the dropped neurons is temporally removed and they do not have an impact on the model’s performance.

Dropout regularization will ensure the following:

The neurons can’t rely on one input because it might be dropped out at random. This reduces bias due to over-relying on one input, bias is a major cause of overfitting.

Neurons will not learn redundant details of inputs. This ensures only important information is stored by the neurons. This enables the neural network to gain useful knowledge which it uses to make predictions.

References

https://www.section.io/engineering-education/dropout-regularization-to-handle-overfitting-in-deep-learning-models/#getting-started-with-dropout-regularization