L2 Norm

L2 Norm is a mathematical concept used in Data Science and Artificial Intelligence to measure the magnitude of a vector. It is also known as Euclidean Norm. L2 Norm is calculated by taking the square root of the sum of the squared values of each element in the vector. It is commonly used in machine learning algorithms to calculate the distance between two data points in a high-dimensional space. L2 Norm is also used in regularization techniques to prevent overfitting in models by adding a penalty term to the loss function. In image processing, L2 Norm is used to normalize pixel values to a range of 0 to 1.
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