Hyper-Parameter Optimization (HPO): Automated ML

Picking the right learning rate, batch size, and number of layers is often called “alchemy.” Hyper-Parameter Optimization (HPO) turns this into a rigorous mathematical search.

Common Strategies

• Grid Search: Trying every possible combination of settings (expensive and slow).
• Random Search: Often more efficient than grid search, as it explores the space more broadly.
• Bayesian Optimization: Uses a probabilistic model to predict which settings are likely to work best, focusing the search where it’s most needed.

Auto-ML

HPO is the foundation of “Auto-ML” tools, which aim to let users upload a dataset and receive a fully optimized model without writing a single line of deep learning code.