Grid Search vs Random Search Grid search tries out all the possible combinations of hyperparameters, but not in the whole parameter space, only in a predefined grid. Using the previous example, we will define a grid for the number of layers, a grid for the number of neurons in each layer, and a grid for the learning rate. Then, grid search systematically evaluates each combination of hyperparameters and selects the one that yields the best performance on a validation set. On the other hand, we have random search, which, as the name suggests, randomly samples combinations of hyperparameters from the hyperparameter space. This method is less computationally expensive than grid search because it doesn't evaluate all possible combinations and instead explores a random subset of the hyperparameter space. Nevertheless, while this randomness might seem less systematic, it often performs surprisingly well and can be more efficient than grid search, especially in high-dimensional spaces. However, both grid search and random search have their advantages and disadvantages. Grid search guarantees that you'll find the optimal combination of hyperparameters within the specified grid, but it can also totally miss the space where our model best performs, as is depicted here. Additionally, it can be computationally expensive, especially if the hyperparameter space is large. On the other hand, random search is less computationally demanding and can often find good hyperparameter configurations within fewer evaluations, but there is no guarantee that you will find the optimal solution.