What are advanced parameter techniques in AI?

What are advanced parameter techniques in AI?

Advanced parameter techniques in AI involve optimizing model performance through fine-tuning hyperparameters, using techniques like grid search, random search, and Bayesian optimization.

Can you explain how Bayesian optimization works?

Bayesian optimization is a probabilistic model-based approach that uses a surrogate model to predict the performance of different hyperparameter configurations and selects the most promising ones to evaluate.

What are some common hyperparameters I should tune?

Common hyperparameters include learning rate, batch size, number of layers, and dropout rate, among others, depending on the model.

How do I know if my model is overfitting?

You can check for overfitting by comparing the training and validation loss; if the training loss decreases while the validation loss increases, your model may be overfitting.