Optimizing Neural Network: An Overview of Dropout

Photo by Pranav Prakash on Unsplash

Dropout is a regularization technique commonly used in neural networks during training. It involves randomly deactivating (dropping out) a fraction of neurons during each training iteration. This helps prevent overfitting and enhances the model’s generalization performance. Let’s delve into the details of dropout:

I. Objective of Dropout:

Preventing Overfitting:

• Dropout is primarily employed to mitigate overfitting, which occurs when a model becomes too specialized on the training data and fails to generalize well to new, unseen data.

II. Dropout Mechanism:

Random Deactivation:

• During training, a specified fraction of neurons (chosen randomly) is deactivated (set to zero) on each forward and backward pass.
• The deactivated neurons do not contribute to the computation of the output and gradients.

III. Implementation in Neural Networks:

1. Dropout Layer:

• In neural network architectures, dropout is typically implemented as a dropout layer.
• The dropout layer is inserted between other layers of the network.