Optimization and Hyperparameter Tuning
Focus is placed on improving model performance through systematic tuning. Students study learning rates, batch sizes, and regularization parameters. Techniques such as grid search and Bayesian optimization automate configuration. Proper tuning can drastically enhance accuracy and convergence speed. It explores balancing complexity and generalization. Optimization skills are essential in professional ML workflows.
Tuning Components:
- Hyperparameter search methods
- Regularization and control
- Performance optimization
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