Nested Learning reframes neural models as multi-loop systems updating at different frequencies, revealing that depth stacking hides gradient mechanics and limits continual learning. It interprets optimizers like Momentum and Adam as associative gradient memories and introduces CMS for incremental abstraction. The HOPE module combines self-modification, multi-clock updates, and deep contextual compression, offering a white-box path beyond static backbones for long-context and

Optimize ML models with Grid Search, Random Search, and Bayesian Optimization. Boost performance, reduce overfitting, and enhance metrics.