Standard 2 stops to get here · leads to 3
Normalization
Scaling features to a standard range (typically 0-1 using min-max scaling) to improve model training and convergence. Often used interchangeably with standardization (mean=0, std=1), though technically distinct.
Your route here
2 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Feature ✓ understood
A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.
- Normalization · you are here ✓ understood
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Feature Normalization
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Foundations Data Preprocessing Cleaning, transforming, and preparing raw data for model training (handling missing values, normalization, encoding). Neural Networks Batch Normalization A technique that normalizes layer inputs to stabilize and accelerate training by reducing internal covariate shift. Vision & Multimodal Image Normalization Scaling pixel values to standard ranges (e.g., mean=0, std=1) to improve training. Foundations Feature Engineering The process of selecting, transforming, and creating input features to improve model performance.