Batch Normalization in Convolutional Neural Networks | Baeldung on Computer Science

Training Deep Neural Networks is a difficult task that involves several problems to tackle. Despite their huge potential, they can be slow and be prone to overfitting. Thus, studies on methods to solve these problems are constant in Deep Learning research. Batch Normalization – commonly abbreviated as Batch Norm – is one of these methods. Currently, it is a widely used technique in the field of Deep Learning. It improves the learning speed of Neural Networks and provides regularization, avoiding overfitting. But why is it so important? How does it work? Furthermore, how can it be applied to non-regular networks such as Convolutional Neural Networks? To fully understand how Batch Norm works and why it is important, let’s start by talking about normalization. Normalization is a pre-processing technique used to standardize data. In other words, having different sources of data inside the same range. Not normalizing the data before training can cause problems in our network, making it dras

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