Data scaling, also known as feature scaling or normalization, is a preprocessing step in neural networks to transform input data into a specific range or distribution. It involves adjusting the scale of input features to ensure that they have similar magnitudes or are on a comparable scale. Data scaling is performed to improve the training process and the overall performance of the neural network.
When features have different scales or units, it can lead to issues during training, as some features may dominate the learning process due to their larger numerical values. This can result in slower convergence, instability, or biased weight updates. By scaling the features, the network can achieve a more balanced learning process.
Common techniques for data scaling include min-max scaling (also known as normalization), where the features are scaled to a specified range, typically between 0 and 1. Another approach is standardization, where the features are transformed to have zero mean and unit variance. Standardization helps center the data around zero and ensures that the features have comparable variances.
Data scaling is an essential preprocessing step that promotes stable training, improves convergence, and enhances the performance of neural networks by providing inputs that are better suited for learning algorithms.