Fine-tuning Models: Hyperparameter Optimization

Hyperparameter optimization is a crucial aspect of machine learning that involves systematically exploring the most suitable set of hyperparameters to enhance a model's performance. These hyperparameters, distinct from model parameters, are predetermined and can significantly influence the model's outcome. Techniques such as grid search, random search, and Bayesian optimization are employed for hyperparameter optimization. The Adam optimizer is an efficient method for fine-tuning models to perfection. Hyperparameter tuning plays a vital role in preventing overfitting by controlling the balance between model complexity and generalization capability. Regularization techniques like L1 and L2 regularization can also help prevent overfitting.