Backpropagation

Category: science

The algorithm used to calculate the gradient of the error function in neural networks.

Backprop is the "correction signal." After the neural net makes a guess, the math tells it how *wrong* it was. Backprop propagates that error signal back through every node, telling each one how to adjust its weight to make a smaller error next time.

Common Examples

• Modern deep learning frameworks handle the complex backpropagation math automatically, allowing us to focus on data structure design.
• If backpropagation fails to converge, it usually indicates a flaw in your model architecture or an improperly normalized input dataset.