Dropout — Google Cloud ML Engineer Practice Questions
Dropout is a regularization technique used during neural network training where a random fraction of neurons are temporarily deactivated on each forward pass, forcing the network to learn redundant representations and reducing co-adaptation between neurons. At inference time all neurons are active, but their outputs are scaled to account for the dropout rate applied during training. The Google Cloud ML Engineer exam covers dropout in the context of custom TensorFlow and PyTorch models trained on Vertex AI, where candidates must understand how to set appropriate dropout rates, recognize dropout as a remedy for overfitting in deep learning models, and distinguish it from other regularization approaches like L2 weight decay.
Free questions on dropout
Which technique helps prevent overfitting in neural networks by randomly deactivating neurons during training?
Free question · easy · full answer + explanation
More dropout questions in the full bank
- What does dropout regularization do in a deep neural network? Unlock answer & explanation →
- When training a deep learning model on Vertex AI, which optimization technique helps reduce overfitting while maintaining model performance? Unlock answer & explanation →