AI Optimization - Gradient Descent & Hyperparameter Tuning

Validation sets are used to evaluate different hyperparameter configurations during model development, allowing you to select the settings that yield the best performance on unseen data.

It's crucial to track metrics such as loss, accuracy, precision, and recall during both training and validation to identify potential issues like overfitting or underfitting.

The optimal optimizer depends on the specific problem you're solving; options include Adam, SGD, and RMSprop, each with different strengths and weaknesses regarding convergence speed and stability.

Regularization methods like L1 or L2 regularization, dropout layers, and early stopping are commonly used to reduce the model's complexity and improve its generalization ability.