Web Reference: In artificial neural networks, batch normalization (also known as batch norm) is a normalization technique used to make training faster and more stable by adjusting the inputs to each layer—re-centering them around zero and re-scaling them to a standard size. Jul 23, 2025 · Batch Normalization is used to reduce the problem of internal covariate shift in neural networks. It works by normalizing the data within each mini-batch. This means it calculates the mean and variance of data in a batch and then adjusts the values so that they have similar range. Sep 3, 2025 · This article provided a gentle and approachable introduction to batch normalization: a simple yet very effective mechanism that often helps alleviate some common problems found when training neural network models.
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