The primary goal of hyperparameter tuning is to find the optimal values of hyperparameters that result in the best performance of a machine learning model on a given task.

Activation function is a property of the model architecture and not a hyperparameter.

Grid search is the most common method for hyperparameter tuning due to its simplicity and ease of implementation.

Grid search can be computationally expensive, especially for models with a large number of hyperparameters.

F1 score is a common metric for evaluating the performance of a machine learning model during hyperparameter tuning because it takes into account both precision and recall.

Cross-validation is used in hyperparameter tuning to estimate the generalization error of a machine learning model, which is the error that the model will make on new, unseen data.

Early stopping is a common technique for reducing overfitting in hyperparameter tuning. It involves stopping the training process before the model starts to overfit the training data.

Regularization is used in hyperparameter tuning to reduce the variance of the model's predictions, which makes the model more robust to noise and outliers in the data.

Parallel processing is a common technique for improving the computational efficiency of hyperparameter tuning. It involves running multiple hyperparameter tuning experiments in parallel on different machines or cores.

Transfer learning is used in hyperparameter tuning to transfer knowledge from a pre-trained model to a new model, which can help to improve the performance of the new model and reduce the amount of data needed to train it.

Keras Tuner is a common tool for performing hyperparameter tuning in machine learning. It is a Python library that provides a simple and easy-to-use interface for hyperparameter tuning.

AutoML is used in hyperparameter tuning to automate the process of hyperparameter tuning, which can save time and effort and help to improve the performance of machine learning models.

Overfitting is a common challenge in hyperparameter tuning. It occurs when a machine learning model learns the training data too well and starts to make predictions that are too specific to the training data and do not generalize well to new, unseen data.

The purpose of hyperparameter optimization in machine learning is to find the optimal values of hyperparameters that result in the best performance of a machine learning model on a given task.

Bayesian optimization is a common technique for hyperparameter optimization. It is a sequential model-based optimization algorithm that uses a probabilistic model to guide the search for the optimal values of hyperparameters.