Artificial Intelligence Programming 2025 – 400 Free Practice Questions to Pass the Exam

Back-propagation is an essential algorithm used primarily for training artificial neural networks. Its fundamental purpose is to minimize the error of the model by adjusting the weights of the connections in the network based on the difference between the predicted output and the actual target values.

This process involves two main steps: the forward pass and the backward pass. During the forward pass, the input data is fed into the network, and the output is computed. The backward pass then involves calculating the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward through the network. This enables the model to update its weights systematically, which leads to improved performance over successive iterations.

The other options do not utilize back-propagation as their primary training mechanism. Decision trees are typically built through recursive partitioning and do not require gradient-based optimization. Support vector machines focus on maximizing the margin between data points and do not employ back-propagation. Genetic algorithms are optimization techniques based on natural selection principles that do not involve neural networks or back-propagation.