training deep leaning models through each possible combinationand the combination of the hyperparameters with the best performance is the final choice with grid searchthere is possibility of not having the best possible combination of hyperparametersas the permutations are limited to the provided grid or are computationally very expensiveif large number of choices are added to the grid random search usually tends to fair better with hyperparameter tuning with random searchthe possibilities of having the best combination of hyperparameters for the model are higher with fairly lower number of combinations (though not guaranteedtuning hyperparameters is often iterative and experimental odel capacity let' briefly revisit the notions of model capacityoverfittingand underfitting we will use the previous example of fitting regression model (refer to we have data of the form {( )( )(xnyn)}where rn and rand our task is to generate computational procedure that implements the function we measure performance over this task as the root mean squared error (rmseover unseen dataas follows yi xi du  given dataset of the form {( )( )(xnyn)}where rn and rwe use the least squares modelwhich takes the form bx where is vector such that is minimized herex is matrix where each row is an the value of can be derived using the closed form (xtx)- xty