I've read as many topics regarding hyperparameter tuning as I could, and I developed the following algorithm for hyperparameter tuning & final model building
- Split the data in train set (80%) & test set (20%)
- perform a k-fold cross-validation in the train set n times, changing the hyperparameters each time and choosing the ones that performed better in average on the validation sets.
- build the neural networks that performed better in step 2 (let's say, the top 3) and train with the whole train set (80%)
- Feed the NNs built in 3 with the test set and pick up the one that performed better.
I see no flaws in this process, however recently I've been reading about nested CV and I don't know when to use it instead of the steps proposed here.