# Backward propagation of errors (Back propagation)

This is the specific implementation of gradient decent applied to neural networks . There are two stages of this calculation:

• Forward pass through as you evaluate the neural network on test data .
• Backward propagation of that error as you use the chain rule for differentiation on each of the perceptrons .

For this to work all the perceptrons need to have differentiable activation function .