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Advancing Hyperparameter Optimization
This chapter examines the evolution of hyperparameter optimization techniques, comparing traditional methods like random search to advanced strategies such as Bayesian optimization. It highlights the innovative G2G algorithm, which merges random search with gradient-based approaches, and discusses the implications of these advancements for optimizing large models.