Backpropagation is how neural networks learn. It calculates how much each weight contributed to the error, then adjusts weights to reduce that error.
Think of it as:
• Credit assignment - Who's responsible for the error?
• Backward flow - Error flows from output to input
• Chain reaction - Each layer affects the next
• Gradient calculation - How to improve each weight
🏭 Factory Analogy
Imagine a factory where each worker (neuron) passes products to the next. If the final product is defective, we trace back through each step to find who contributed to the defect and how to fix it.
How It Works
Step 1: Forward Pass
Input flows forward through the network to produce output
Step 2: Calculate Error
Compare output with target to get the loss
Step 3: Backward Pass
Error flows backward, layer by layer
Step 4: Calculate Gradients
Find how each weight affects the error
Step 5: Update Weights
Adjust weights to reduce error
See Backpropagation in Action
←←←
X₁
∂L/∂X₁
X₂
∂L/∂X₂
Input Layer
H₁
∂L/∂H₁
H₂
∂L/∂H₂
H₃
∂L/∂H₃
Hidden Layer
Y
∂L/∂Y = 1
Output Layer
Key Insight:
The gradient at each layer depends on all the gradients after it. This is the chain rule in action!