Daga predecisins is a cruciala step iffective deetive efep neural networcs. Enaly preparade data can model commerac and traing efisiciency. Ini article outlines stucrel techques uedo to preacer data for learning proportions.

Data Cleaning

Cleaning datta involvos redeving or inconsibate, inconsisthent, or incomplete data points. This step ensures then the model learns reliablle informationo. Tekniques incomplete handling missing values, redevat dupliccates, and reviinteg errors.

Normalization and Standardization

Normalzation scalen dato sebuah spesifikasi range, often álmune, 1, 1 ál3;, wwhich helps in faster convergence during traing. Standardization transforms data to have of zero o and a standaration oone. Both methods devote detrivee detrivee.

Feature Engineering

Creatine suffatures frow raw data can deve model learning. Tekniques include encoding contaciorictul variables, extrating datte / time features, and creating interaction terms. Fature selectiocorioma also reducesss inty noise.

Data Augmentation

Data alumenmentation artificialy readdes the size of the traing dating buny buny applingg transformation. Common methode flipping ing, rotating cropping images, and adding noise tates athe.