Data Preprocessing
Domain: Data & Datasets
🎯 Motivation
Transforming raw data into a format suitable for training: tokenizing, normalizing, augmenting, splitting.
📋 Concrete Example
Preprocessing: raw text -> tokenize -> lowercase -> remove punctuation -> pad sequences -> split into train/test. Raw data becomes training-ready.
🔗 Analogy
Prepping ingredients before cooking - wash, peel, chop, measure. Raw ingredients become cooking-ready. Data preprocessing makes raw data training-ready.