Video: The backpropagation algorithm explained visually (3:45)
The Learning Process
Imagine you're trying to learn to shoot basketball free throws. You shoot, miss, and adjust. Neural networks learn the same way - they make predictions, check how wrong they were, and adjust their weights.
The Forward and Backward Pass
1. Forward Pass
Input flows through the network
Calculate output
2. Compute Loss
How wrong was the prediction?
Compare to target
3. Backward Pass
Calculate gradients
Error flows backward
4. Update Weights
Adjust to reduce error
Descend the gradient
The Chain Rule: Error Attribution
Backpropagation uses the chain rule from calculus. If changing weight A affects neuron B, which affects output C, then:
Chain Rule Visualization
We multiply partial derivatives to find how much each weight contributed to the error
💡 Intuition
Think of it like a game of telephone. If the final message is wrong, we trace back through each person to see who contributed most to the error. We then tell them to adjust their message slightly.
Interactive Backprop Calculator
Try calculating a simple backpropagation step: