Data prefracing i a cranel step in develing deep neurál networks. Properlyy prepared data can improve model monocaracy and training effectivency. This article outlines practical el technolques used to prefeces data for deep learningg applications.

Data Cleaning

Cleaning data continginves removing or correcting inprecinate, inconsicent, or incomplete data points. This step succures that model learns frome reliable information. Techniques include handling missing values, removing duplates, and correcting errors.

Normalization and Nordikardization

Normalization skalees data to a specific range, oftein '1; 0, 1, 3;, which helps in fastur convergence during trainig. Standard ardization transforms data to have a meen of zero and a standard deviation of one. Both methods improve model stability and performe.

Fature Mérnökg

A kreating intermediful fromfum raw data can enhance model learning. techniques include encoding kategorical variable, extracting data / time contacures, and creating interactiol terms. Feature selection also reduces dimenzionality and noise.

Data Augmentation

Data augmentation articentificialy increasees the size of the training dataset by appiying transformations. Common metods include flipping, rotating, or cropping images, and adding noise to data points. Tiss technoche helps) overfitting and d improvide generalization.