Non-surprisingly, the previous section illustrates that model complexity and training set size have an impact on the accuracy of the final model. Using the same training database, it is clear after a few (10-100s) epochs which set of hyperparameters performs better in training (see Figure ). Such an assumption is not universally valid as some setups could converge slower but to more accurate values. Nevertheless, with this approach, we should be able to find the model that most rapidly converges to low MAEs. Therefore, in this section, we seek hyperparameters that reach the lowest validation MAEs after 200 epochs.