What is Backpropagation
A neural network training algorithm that calculates how to change model weights to reduce error.
Definition
Error backpropagation is one of the key mechanisms for training neural networks. First, the model makes a prediction, then the error is calculated, after which the algorithm goes back through the layers and determines the contribution of each parameter to this error. Based on these gradients, the optimizer updates the weights.
Example
The neural network made a mistake in classifying the image. Backpropagation helps you figure out which weights need to be changed so that the error is smaller next time.
Why it matters
The term is important for understanding how a neural network does not just produce an answer, but gradually learns from examples.
How it works
The algorithm applies the chain rule from mathematical analysis to efficiently compute gradients for all parameters of a multilayer network.
Where it is used
- neural network training
- deep learning
- model optimization
Limitations
The method requires differentiable operations and may experience decay or explosion of gradients. Quality also depends on the data and the optimizer.