The code generated in the video can be downloaded here:
https://github.com/bnsreenu/python_for_microscopists/blob/master/302-Tuning%20deep%20learning%20hyperparameters/302-Tuning%20deep%20learning%20hyperparameters%E2%80%8B.py

All other code:
https://github.com/bnsreenu/python_for_microscopists

GridSearchCV's grid search exhaustively generates candidates from a grid of parameter values specified with the param_grid parameter.

The GridSearchCV instance when “fitting” a dataset, everything possible
combinations of parameter values are evaluated and the best combination is retained.

cv parameter can be defined for the cross-validation splitting strategy.

GridSearch is designed to work with sklearn models. But we can also use it to tune hyper-parameters for deep learning, at least for Keras models.

Example of breast cancer in Wisconsin
Link to dataset: https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data

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