I’ve been playing around with my new, improved data set, and tried out a few configurations on Google Colab. Had my first real experience with overfitting. With one MLP configuration I was getting really good loss figures on the training data, and a really bad result on the test data. I believe this is a classic sign of overfitting. The losses on the training data were amazing compared with the losses using a less complex architecture (and also compared with simple Linear Regression), but it didn’t carry through. I guess this is where the trial and error starts. Find that best result that still works with test data.