Data Engineering
Normalization
Scaling all your data to a consistent range so that big numbers don't dominate small ones during AI training.
Definition
Data preprocessing techniques that transform features to a common scale (e.g., 0-1 or standard deviation). Includes min-max scaling, z-score normalization, and batch normalization in neural networks.
Why it matters
A simple step that dramatically improves training speed and model performance, often the highest-ROI preprocessing step.
Related terms in Data Engineering
Batch Processing
Processing a large group of data all at once on a schedule, rather than one piece at a time in real-time.
Chunking Strategies
Chopping up long documents into small, bite-sized pieces so an AI can search and read them easily.
Data Augmentation
Creating fake but realistic training examples (like flipping or rotating images) to give the AI more data to learn from.
Data Labeling
The human work of tagging data with correct answers so an AI can learn from it, like marking photos as "cat" or "dog."
From vocabulary to outcomes
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