Here are some of the most common uses of L2 normalization:

    1. Image recognition: L2 normalization is used to normalize the feature vectors of images, making sure that the features have a consistent scale and magnitude.
    2. Natural language processing: In NLP, word vectors are often L2 normalized to ensure that the magnitude of the vector reflects the importance of the word in the corpus.
    3. Machine learning: L2 normalization is used in some machine learning algorithms, such as k-nearest neighbors, to normalize the feature vectors and make the algorithms scale invariant.
    4. Deep learning: L2 normalization is commonly used in deep learning models to prevent the activation values in a neural network from becoming too large and unstable.
    5. Recommendation systems: L2 normalization is used to normalize the feature vectors in recommendation systems, making sure that the algorithms do not favor users or items with large feature values.
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