What is the impact of normalization on deep learning?

What is normalization in the context of deep learning?

Normalization refers to techniques that adjust the input data or the outputs of layers in a neural network to improve training stability and performance.

How does normalization affect the training of deep learning models?

Normalization can lead to faster convergence, reduce the risk of overfitting, and help the model generalize better to unseen data.

Are there different types of normalization techniques?

Yes, common techniques include Batch Normalization, Layer Normalization, and Instance Normalization, each serving different purposes.

When should I use normalization in my model?

Normalization is generally recommended when training deep learning models, especially when using activation functions sensitive to input scale.