What is dropout and batch normalization?

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Accepted Answer

Dropout is a regularization technique to prevent overfitting by randomly setting some neuron outputs to zero during training. Batch normalization is a technique to improve the training of deep neural networks by normalizing the input of each layer.

Dropout randomly sets a fraction of neuron outputs to zero during training to prevent overfitting.
Batch normalization normalizes the input of each layer to improve training of deep neural networks.
Dropout helps in reducing overfitting by preventing complex co-adaptations on training data.
Batch normalization reduces internal covariate shift and allows higher learning rates.
Example: In a neural network, dropout can be applied after each hidden layer during training.
Example: Batch normalization can be added after the activation function in each layer of a neural network.

What is dropout and batch normalization?

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