Hyperparameter Optimization
Tune model hyperparameters for optimal performance.
Hyperparameter optimization can significantly improve model performance. This guide covers systematic approaches to finding optimal configurations.
Hyperparameters vs Parameters
- **Parameters**: Learned during training (weights, biases)
- •Hyperparameters: Set before training (learning rate, batch size, architecture)
Common Hyperparameters
Key hyperparameters to tune:
- •Learning rate
- •Batch size
- •Number of layers/units
- •Dropout rate
- •Regularization strength
- •Optimizer choice
Search Strategies
Grid Search: Exhaustive search over parameter grid. Simple but expensive.
Random Search: Sample randomly from parameter space. Often more efficient than grid search.
Bayesian Optimization: Uses probabilistic model to guide search. Most sample-efficient.
Population-Based Training: Evolutionary approach for training neural networks.
Tools and Frameworks
Popular optimization tools:
- •Optuna: Flexible and efficient
- •Ray Tune: Distributed hyperparameter tuning
- •Weights & Biases Sweeps: Integrated with experiment tracking
- •Hyperopt: Bayesian optimization
Best Practices
- Start with wide search, then narrow
- •Use early stopping
- •Log all experiments
- •Consider compute budget
- •Use cross-validation
Learning Rate Scheduling
Learning rate is often the most important hyperparameter:
- •Start with 1e-3 or 3e-4
- •Use learning rate finder
- •Implement scheduling (cosine, step decay)
Automated ML (AutoML)
For comprehensive automation, consider AutoML tools that handle both architecture and hyperparameter search.