Part 21: Backpropagation: How Neural Networks Learn From Their Mistakes

Thank you for pointing this out! 🙌 I agree that the basketball analogy leaves out an important part of the process. The real challenge is credit assignment: figuring out how much each weight, especially in the earlier layers, contributed to the final error.

I really like how you connected this to the chain rule and vanishing gradients. Thinking about it this way makes those concepts easier to understand and creates a natural link to the topics that follow. Thanks for adding this valuable insight to the discussion!

A tiny two-layer numeric example could make that connection concrete. Compute the loss once, perturb one early weight by a small amount, and compare the observed loss change with the derivative produced by the chain rule.

Then repeat with a saturated activation. The smaller gradient becomes something a beginner can inspect, while the distinction between computing a gradient and applying the optimizer update stays visible. It would extend the analogy without suggesting that an early layer directly observes the final mistake.