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

Backpropagation is a fundamental algorithm used in training neural networks, specifically for adjusting the weights of the model to minimize the error in its predictions. The primary role of backpropagation is to compute gradients, which are crucial for the optimization process. During training, a neural network makes predictions and then calculates the error between the predicted output and the actual target output. Backpropagation works by propagating this error backward through the network.

This process involves applying the chain rule of calculus to compute the gradient of the loss function with respect to each weight in the network. These gradients indicate the direction and magnitude by which the weights should be adjusted to decrease the error. Based on these gradients, optimization algorithms such as gradient descent are employed to update the weights in a way that incrementally reduces the prediction error over many iterations.

Other options do not describe the function of backpropagation accurately. Initializing the weights of the model is a separate step that occurs before training starts and is not the function of backpropagation. Measuring accuracy pertains to evaluating the model's performance after training rather than adjusting the weights during training. Simplifying the network design is not related to the backpropagation process itself; rather, it refers to the architectural choices made during the design phase of