Was ist Backpropagation

Definition

Backpropagation is the foundational training algorithm for neural networks. It operates by systematically propagating the prediction error backward through the network, enabling the precise adjustment of neuron weights.

Explanation

Once an error is identified during the forward propagation phase, Backpropagation leverages the chain rule of differential calculus to precisely determine each weight's contribution to that overall error. This crucial information is then utilized to incrementally adjust the network's weights, ensuring a reduced error in subsequent iterations. This iterative process is repeated thousands or even millions of times until the model achieves a high level of predictive accuracy. For B2B AI applications, Backpropagation is fundamental. It ensures that predictive models – whether for sales forecasting, deal probability, or other critical business metrics – continuously learn from historical data and refine their performance, delivering increasingly precise and reliable results over time.