Question: How do dropout layers in neural networks help prevent overfitting? By randomly disabling a fraction of the neurons during training By randomly initializing network weights

How do dropout layers in neural networks help prevent overfitting?
By randomly disabling a fraction of the neurons during training
By randomly initializing network weights
By normalizing the input features
By compressing the size of the network
How do dropout layers in neural networks help

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