Artificial Intelligence Programming Practice Exam

What does back-propagation minimize in neural networks?

1. The amount of data processed

2. The objective function

3. The training time

4. The number of hidden layers

The correct answer is: The objective function

Back-propagation is a fundamental algorithm used in training neural networks, and it specifically aims to minimize the objective function, which is often referred to as the loss or cost function. This function quantifies how well a neural network's predictions match the actual target values from the training data. By adjusting the weights of the neural network through the back-propagation algorithm, the model iteratively reduces the difference between predicted values and true values, thus minimizing the objective function. The objective function typically measures the error, such as mean squared error or cross-entropy loss, depending on the specific task (regression or classification). Through the process of back-propagation, gradients of the objective function with respect to each weight are computed, allowing for an efficient update of the weights to improve model performance. This process is critical because a well-minimized objective function leads to a better-performing model, ensuring that the neural network can make accurate predictions when presented with new, unseen data.