 - Pros: Maps out the problem space and provides more opportunity for optimization
 - Cons: Can be slow to run for large numbers of hyperparameter values

University of Michigan - Ann Arbor

Grid search is slightly more sophisticated than manual tuning. It involves systematically testing multiple values of each hyperparameter, by automatically retraining the model for each value of the parameter. For example, you can perform a grid search for the optimal batch size by automatically training the model for batch sizes between 10-100 samples, in steps of 20. The model will run 5 times and the batch size selected will be the one which yields the highest accuracy.

Grid Search for Hyperparameter Tuning Methods in Deep Learning

A helpful website that introduces neural networks:
https://missinglink.ai/guides/neural-network-concepts/

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