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Normalize is a term used in various fields to describe the process of adjusting data or values to a common scale or range. This adjustment is often done to make comparisons easier, to improve the performance of algorithms, or to standardize measurements. In statistics, normalization typically involves scaling data so that it falls within a specific range, such as 0 to 1 or -1 to 1. This can be achieved through methods like min-max scaling or z-score standardization. Min-max scaling transforms data by subtracting the minimum value and dividing by the difference between the maximum and minimum values. Z-score standardization, on the other hand, centers the data around zero and scales it by the standard deviation, resulting in values that represent how many standard deviations a data point is from the mean.

In image processing, normalization is applied to pixel values to enhance contrast or to prepare images for analysis. This might involve stretching the histogram of pixel intensities to cover the full dynamic range. In machine learning, normalizing input features is a crucial preprocessing step. Many algorithms, such as gradient descent-based methods and support vector machines, perform better when features are on a similar scale. This prevents features with larger magnitudes from dominating the learning process. The specific method of normalization used depends on the nature of the data and the requirements of the application. Understanding the concept of normalization is fundamental for data analysis, scientific research, and the development of effective computational models.