10.4.3 Backpropagation (High-level)

Imagine you're playing a game, like throwing a ball at a target. At first, you might miss a lot. But after each try, you think about what went wrong: Was your arm too high? Did you throw too hard? You then adjust how you throw the ball for your next try. Over time, you get better and better at hitting the target!

In computer science, especially with something called "neural networks" (which are like digital "brains" that learn), we have a similar process called Backpropagation.

Think of a neural network trying to solve a puzzle, like telling the difference between a picture of a cat and a dog. When it first starts, it's not very good. It might guess "cat" when it's actually a dog!

The "Mistake" Part: When the network makes a guess, we compare its guess to the correct answer. The difference between its guess and the correct answer is its "mistake" or "error."
The "Learning Backwards" Part: Backpropagation is how the network uses that "mistake" to learn. Instead of just stopping, the network sends information about its mistake backward through its "brain." It figures out which parts of its "brain" (called "weights" and "biases") contributed most to the mistake.
The "Adjusting" Part: Once it knows which parts were "wrong," it slightly adjusts those parts. It's like you adjusting your throwing arm after missing the target. These adjustments help the network make a better guess next time.

This process of guessing, finding the mistake, and then adjusting itself backward happens thousands or even millions of times! Each time, the network gets a tiny bit smarter, until it can accurately tell the difference between cats and dogs. So, backpropagation is super important because it's the main way these digital "brains" learn from their mistakes and improve over time.