What is Backpropagation?
Backpropagation (short for backward propagation of errors) is the mathematical process used by a neural network to calculate exactly how much each of its millions of internal parameters was responsible for a mistaken prediction.
How does it work?
When a network makes a prediction, the result is compared to the correct answer to calculate the total error. Backpropagation takes that total error and works backward through the network's layers, using calculus (the chain rule) to assign a specific portion of the blame to every single connection.
What is it commonly confused with?
It is crucial to distinguish backpropagation from gradient descent:
- Backpropagation simply calculates the gradients (figuring out who is to blame for the error).
- Gradient descent (the optimizer) uses those calculated gradients to actually update the parameters and improve the model.
Why does it matter?
Before backpropagation was widely adopted, it was incredibly difficult to figure out how to train multi-layered neural networks effectively. Backpropagation provided the efficient mathematical breakthrough that made training deep learning models computationally possible.