Pick the wrong loss function and your model optimises the wrong thing — perfectly. The loss is the single number training tries to shrink, so it quietly defines what "wrong" even means. I built an interactive visualiser of MSE, MAE, and cross-entropy so you can see why the choice matters. 🎯 Drag the prediction: https://dev48v.infy.uk/dl/day6-loss-functions.html This is Day 6 of DeepLearningFromZero. Loss = one number for "how wrong" The network's output is compared to...