Preprocessing data ensures compatibility and improves the performance of neural networks. Standard steps include cleaning, normalizing, and encoding data.
For numeric data, normalization or standardization scales features to comparable ranges, preventing dominance by large values. For categorical data, one-hot encoding or label encoding transforms categories into numerical formats. Text data requires tokenization, stopword removal, and possibly stemming or lemmatization. For images, resizing and augmentation like flipping or rotating enhance diversity.
Splitting data into training, validation, and test sets helps evaluate the model's generalization. Techniques like feature scaling and dimensionality reduction improve computational efficiency and prevent issues like exploding gradients.