What is Dropout & Batch Normalization?

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Dropout is a regularization technique to prevent overfitting by randomly setting some neuron outputs to zero during training. Batch Normalization is a technique to normalize the inputs of each layer to improve training speed and stability.

Dropout randomly sets a fraction of neuron outputs to zero during training to prevent overfitting.
Batch Normalization normalizes the inputs of each layer to improve training speed and stability.
Dropout is commonly used in neural networks to improve generalization.
Batch Normalization helps in reducing internal covariate shift and allows higher learning rates.
Example: In a neural network, Dropout can be applied after each hidden layer with a dropout rate of 0.5.
Example: Batch Normalization can be added after the activation function in each hidden layer.

What is Dropout & Batch Normalization?

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What is Dropout &amp; Batch Normalization?

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What is Dropout & Batch Normalization?