normalizationaugmented
Normalization augmented refers to a data preprocessing technique used in machine learning and statistical analysis. It combines the principles of data normalization with an augmentation process to enhance the quality and diversity of training datasets. Normalization itself aims to scale numerical features to a common range, typically between 0 and 1 or with a mean of 0 and a standard deviation of 1. This helps algorithms that are sensitive to the scale of input features, preventing those with larger values from dominating the learning process.
Augmentation, on the other hand, involves creating new, synthetic data points from existing ones. This is particularly
Normalization augmented applies these two concepts sequentially or in conjunction. For instance, one might first normalize