How Does Back-Propagation Work in Neural Networks?

Demonstrating how background works in Neural Networks, using an example

Neural Networks learn through iterative tuning of parameters (weights and biases) during the training stage. At the start, parameters are initialized by randomly generated weights, and the biases are set to zero. This is followed by a forward pass of the data through the network to get model output. Lastly, back-propagation is conducted. The model training process typically entails several iterations of a forward pass…