What is the primary goal of backpropagation in training neural networks?

The primary goal of backpropagation in training neural networks is to optimize weights and minimize the loss function. Backpropagation is an algorithm used in the training of artificial neural networks, where it calculates the gradient of the loss function with respect to each weight by applying the chain rule. This process allows the network to learn from the errors made in predictions by adjusting the weights in a way that reduces these errors in future predictions.

When the network makes a prediction, it compares the predicted output to the actual target output to compute the loss, which reflects how far off the prediction was. Backpropagation uses this loss to traverse the network in reverse, updating the weights based on their contribution to the total error. This iterative process of adjusting the weights continues until the model reaches an acceptable level of accuracy or the loss is minimized to a satisfactory level.

Therefore, focusing on optimizing weights directly targets the core of the learning process in neural networks, which is to improve their performance by making more accurate predictions. This is why the focus of backpropagation is fundamentally linked to minimizing the loss function through careful adjustment of model parameters.