Standardize vs Normalize

# Standardize: mean 0, std 1 — assumes Gaussian-ish
x = (x - x.mean()) / x.std()

# Normalize: bound to [0, 1] — for bounded inputs / image pixels
x = (x - x.min()) / (x.max() - x.min())

# Robust: for outlier-heavy data
x = (x - np.median(x)) / (np.quantile(x, 0.75) - np.quantile(x, 0.25))

Why it matters: Wrong choice silently breaks training. Standardize for linear models and anything with L2 regularization. Normalize for fixed-range inputs. Robust when you can’t trust your tails.