Artificial Intelligence Programming Practice Exam

Batch normalization is a technique used in training deep neural networks to improve both the training speed and stability. It achieves this by normalizing the inputs to a layer, which helps to mitigate the issues of internal covariate shift. This shift refers to the changes in the distribution of layer inputs during the training process, as the parameters of the previous layers get updated. When the inputs to a layer are normalized, it allows the network to train faster because it effectively stabilizes the learning process.

When you apply batch normalization, the inputs to each layer are scaled and shifted, allowing for consistent distributions. This often leads to the ability to use higher learning rates without the risk of divergence, ultimately speeding up convergence during training. Additionally, batch normalization can reduce the sensitivity to initialization and act as a form of regularization, which contributes to more stable training dynamics.

In contrast, reducing the overall size of a neural network, enhancing data labeling accuracy, or eliminating the need for dropout are not the primary functions of batch normalization. These aspects relate to different strategies in machine learning, such as model compression, improved labeling techniques, and dropout regularization, respectively. Thus, the main goal of batch normalization remains centered on creating a more efficient and stable training process.