Hyperparameter Tuning
Hyperparameters are configuration settings that control the learning process. Unlike model parameters (weights), they're set before training begins. Finding the right combination can dramatically improve performance.
Key Hyperparameters
Learning Rate
Most important! Controls step size during optimization.
Typical range: 1e-5 to 1e-1
Batch Size
Number of samples per gradient update.
Common: 32, 64, 128, 256
Network Architecture
Number of layers, neurons per layer, activation functions.
Start simple, add complexity
Regularization
Dropout rate, weight decay, L1/L2 penalties.
Prevents overfitting
Tuning Strategies
1. Manual Tuning
Change one parameter at a time. Good for understanding but time-consuming.
2. Grid Search
Try all combinations of predefined values. Exhaustive but expensive.
3. Random Search
Sample random combinations. Often better than grid search with same budget.
4. Bayesian Optimization
Intelligently samples based on past results. Most efficient for expensive models.
Grid Search Example
This shows how grid search explores hyperparameter combinations.
Pro Tip: Use coarse-to-fine search. Start with wide ranges, then zoom in on promising regions.