👉 The tuning project involves refining and optimizing the parameters of a machine learning model to enhance its performance on a specific task. This process includes adjusting hyperparameters, such as learning rates, batch sizes, and regularization strengths, to improve accuracy, reduce overfitting, and ensure the model generalizes well to unseen data. Tuning often involves systematic experimentation, using techniques like grid search or random search, and sometimes automated methods like Bayesian optimization to find the best configuration for the model.
