What is Batch Normalization and Layer Normalization?
Автор: Data Science Made Easy
Загружено: 2025-12-12
Просмотров: 4
Описание: Batch Normalization and Layer Normalization are techniques used in deep learning to stabilize and accelerate training by normalizing inputs to layers, but they differ in how and where normalization is applied. Batch Normalization normalizes activations across a mini-batch, helping reduce internal covariate shift and enabling higher learning rates, which speeds up convergence—ideal for CNNs and large batch training. Layer Normalization, in contrast, normalizes across the features within a single data sample, making it well-suited for models like transformers and recurrent networks where batch sizes may be small or variable. From a business perspective, these normalization methods lead to faster training, improved model performance, and more reliable deployment, ultimately helping organizations build robust AI applications efficiently.
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