Description: Data normalization is a fundamental technique in the field of neural networks used to standardize the range of independent variables or features of the data. This process involves transforming input data to have a common scale, which facilitates the training of machine learning models. Normalization can be performed using different methods, such as min-max normalization, which adjusts values to a specific range, or Z-score normalization, which centers data around the mean and scales it according to its standard deviation. The importance of this technique lies in the fact that neural networks are sensitive to the scale of the data; if features have very different ranges, some may dominate the learning process, leading to suboptimal model performance. By normalizing the data, convergence during training is improved, and the risk of the model getting stuck in local minima is reduced. In summary, data normalization is a crucial step in data preparation for machine learning models, ensuring that all features contribute equally to the model’s learning.
PATROCINADORES
