What is “dropout” in neural networks?
a) A regularization technique used to prevent overfitting
b) A method for increasing the learning rate
c) A way to reduce the number of training epochs
d) A technique for normalizing input data
Answer:
a) A regularization technique used to prevent overfitting
Explanation:
Dropout is a regularization technique used in neural networks to prevent overfitting by randomly “dropping out” or disabling certain neurons during training. This forces the network to become more robust by not relying too heavily on any single neuron.
By randomly dropping out neurons, the model can generalize better to new data because it learns multiple independent representations of the data. Dropout is typically applied during the training phase, and all neurons are used during testing.
Dropout has proven to be an effective way to improve the performance of deep learning models, especially when dealing with large, complex datasets.