Automated Hyperparameter Optimization in Deep Learning: AI-Driven Approaches for Model Efficiency and Accuracy

Deep learning model effectiveness alongside accuracy together with generalization ability depend heavily on proper hyperparameter optimization. Traditional tuning methods such as grid search and random search remain inefficient and expensive when managing high-dimensional search spaces especially due to their execution costs. Various AI-driven approaches in hyperparameter optimization now exist to address traditional limitations through structured automated methods for optimal configuration finding. The article examines four systematic optimization methods consisting of Bayesian optimization as well as evolutionary algorithms together with reinforcement learning alongside gradient-based techniques that execute model performance enhancement along with reduced human involvement. Automated Machine Learning (AutoML) frameworks include a discussion about how hyperparameter tuning plays a crucial role in programming model selection together with automatic adjustments of hyperparameters for creating scalable AI solutions. The advantages of AI-driven optimization persist even though leaders encounter issues with largescale model scalability and computational limitations and limited interpretability within their systems. The study identifies meta-learning as well as federated optimization among newly emerging trends in hyperparameter optimization which show promise to transform deep learning adaptability and efficiency performance. The transformable power of AI-driven hyperparameter optimization enables improved model accuracy and shortened training time and enhanced scalability thus representing a vital element for deep learning advancement throughout multiple industries.