Neural network training encounters challenges from distribution shifts between layers, impacting model convergence and performance. While Batch Normalization (BN) transformed the field, it relies on a simplified assumption of a single Gaussian component per batch. Mixture Normalization (MN) addresses this with a Gaussian Mixture Model (GMM) approach, yet incurs significant computational costs with the Expectation-Maximization (EM) algorithm. Our solution, Contextual Normalization (CN), introduces "contexts" by grouping similar observations for local representation, eliminating the need for context construction algorithms. Learning normalization parameters akin to model weights ensures speed, convergence, and outperforms BN and MN.