Learn the core training loop by following predictions, measuring loss, and explaining how updates reduce error over repeated examples.
Listen to hear this room section by section.
Key Ideas
Work through these sections in order. Each one builds the mental model you need before the checkpoint questions will feel easy.
In supervised learning, the model first sees an input and produces a prediction using its current parameters. That prediction is then compared with the target for that example. The difference between the prediction and the target becomes feedback for learning.
One pass through this loop rarely teaches much by itself. Learning comes from repeating the cycle across many examples so that useful patterns are reinforced again and again.
Check Your Understanding
These checkpoints reinforce the lesson you just read. If one feels fuzzy, reopen the relevant section above before trying again.
Task 1
Arrange the steps that happen in a standard supervised training cycle.
Task 2
Choose the explanation that best captures what loss represents.
Task 3
Write a short plain-language explanation of why parameter updates depend on loss.
Ready To Move On?
Up next: Gradient Descent Intuition