How does dropout regularization work in neural networks?
- It increases the learning rate during training.
- It optimizes the weight initialization process.
- It randomly removes a fraction of neurons during each forward pass.
- It reduces the number of layers in the network.
Dropout regularization is a technique that randomly drops (sets to zero) a fraction of neurons during each forward pass. This helps prevent overfitting by forcing the network to learn more robust features. It doesn't affect the learning rate or layer count.
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