🔄 Backpropagation Step-by-Step
Watch how gradients flow backward through the network
Click "Forward Pass" to start
| Component |
Value |
Gradient |
| Input |
- |
- |
| Weight 1 |
0.5 |
- |
| Hidden |
- |
- |
| Weight 2 |
0.8 |
- |
| Output |
- |
- |
| Loss |
- |
- |
How Backpropagation Works:
- Forward Pass: Calculate output from input through weights
- Calculate Loss: Compare output with target
- Backward Pass: Calculate gradients using chain rule
- Update Weights: Adjust weights opposite to gradient direction
Forward: output = σ(w₂ · σ(w₁ · input))
Loss: L = (output - target)²
Backward: ∂L/∂w = ∂L/∂output · ∂output/∂w (chain rule)
Update: w_new = w_old - α · ∂L/∂w