L2normiin

L2normiin, also known as the Euclidean norm or L2 norm, is a mathematical concept used to measure the size or magnitude of a vector in a multi-dimensional space. It is defined as the square root of the sum of the squares of the vector's components. In a two-dimensional space, for a vector (x, y), the L2 norm is calculated as the square root of (x^2 + y^2). This concept is widely used in various fields such as machine learning, signal processing, and physics.

The L2 norm is particularly useful in machine learning for regularization techniques, where it helps to prevent overfitting by penalizing large weights. In signal processing, it is used to measure the energy of a signal. In physics, it is used to calculate the distance between two points in a space.

The L2 norm is different from the L1 norm, which is the sum of the absolute values of the vector's components. The L2 norm is also different from the L-infinity norm, which is the maximum absolute value of the vector's components.

The L2 norm is a special case of the p-norm, where p is equal to 2. The p-norm is a generalization of the L2 norm, where the sum of the absolute values of the vector's components raised to the power of p is taken, and then the p-th root of the sum is calculated.