Tag: NeuralNetworks

By now, our little exam predictor has grown up quite a bit: At this point, the network looks smart on paper. But now we hit a very human problem: The model can become that kid who memorises last year’s question paper perfectly…and still flops in the real exam. This is…

In Part 1 and Part 2, our little exam predictor learned how to adjust its knobs and walk downhill on the loss landscape. It became good at improving itself, but it still thought in straight lines. Real students, however, do not behave like straight lines. Too much study can hurt,…

In Part 1 we built our tiny exam predictor: And we ended with this very important headache: “We know we must change the weights and bias to make the loss smaller.But which way should we change them, and by how much?” Today we answer exactly that. From Loss to Landscape:…

Let’s start with a tiny drama. You are a teacher.You want to guess how well a student will score in an exam. You have a theory: You want a small system that takes these two numbers: and gives you one number: That is it.No robots, no brain scans, no mysterious…