L2 normalization is a mathematical technique used to rescale the elements of a vector so that they have a magnitude (length) of 1 while preserving their direction. L2 normalization is used in many different fields and applications, including the following:

Machine learning and deep learning: L2 normalization is often used in machine learning and deep learning applications as a way to regularize the inputs to a model. By normalizing the inputs, we ensure that they are in a consistent range and scale, which can help prevent overfitting and improve the generalization performance of the model.

Computer vision: In computer vision, L2 normalization is used to normalize the feature vectors of images. This is done to ensure that the features of an image are in a consistent range, which can make it easier to compare the features of different images.

Natural language processing: L2 normalization is used in natural language processing to normalize the feature vectors of text documents. This is done to ensure that the features of different documents are in a consistent range, which can make it easier to compare the similarity of different documents.

Recommendation systems: L2 normalization is used in recommendation systems to normalize the vectors representing users or items. This is done to ensure that the vectors are in a consistent range, which can make it easier to compare the similarity of different users or items.

Control systems: In control systems, L2 normalization is used to normalize the state variables of a system so that they have a magnitude of 1. This can make it easier to design and implement control algorithms that are robust and insensitive to changes in the state variables.

These are just a few examples of the many different fields and applications where L2 normalization is used. The technique is useful because it provides a way to rescale the elements of a vector so that they are in a consistent range, which can help make it easier to compare and process the data.
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