Strategy
Actions
Hyperparameter Tuning
Finding optimal hyperparameters (LR, batch size, etc.) is crucial. Manual tuning is slow - automated methods are essential.
Methods
- Grid Search: Try all combinations - exhaustive but expensive
- Random Search: Sample randomly - often better than grid
- Bayesian Optimization: Model search space, query intelligently
- Hyperband: Early stopping for bad configs
- Population-Based: Evolve configurations
Key Hyperparameters
- Learning rate (most important!)
- Batch size
- Network architecture (layers, units)
- Regularization strength
- Optimizer choice
- Dropout rate
Tools
- Optuna, Ray Tune, Hyperopt
- Weights & Biases Sweeps
- Scikit-learn GridSearchCV
- Keras Tuner