Dropout is a regularization technique used in neural networks to prevent overfitting. It works by randomly “dropping out” a fraction of neurons during training, meaning that their activations are set to zero. This forces the network to learn more robust features that are not reliant on any single neuron, thereby improving generalization to unseen data. Importantly, dropout is only applied during the training phase; during inference, all neurons are active, allowing the model to utilize the full capacity of the network ³⁴.

The dropout rate, which determines the fraction of neurons to drop, can be adjusted for different layers. A common practice is to use a lower dropout rate closer to the input layer and a higher rate in deeper layers. This flexibility allows for fine-tuning the model’s performance ². Additionally, during training, the remaining active neurons’ outputs are scaled to maintain a consistent expected value of activations between training and inference ⁴.

If you have more specific questions about dropout or its implementation, feel free to ask!