A valid model is f(all inputs imaginable)=2. No weights involved. You can apply as much training as you like, but its still just the value "2" as an output. Th

A valid model is f(all inputs imaginable)=2. No weights involved. You can apply as much training as you like, but its still just the value "2" as an output. There is no representation of the data. ML models are a mix of two things: 1. Patterns defined by the individuals performing the model training. Commonly this is referred to as model architecture, but ultimately its the cobbling together of various items to create a way to get a signal from data in a certain way. (As a side note, thanks Statisticians and Computer Scientists for leading the way here!) 2. Model weights to minimize the uncertainty in those patterns. How that uncertainty is measured is part of (1).