What does backpropagation help achieve in a neural network?

Backpropagation is a fundamental algorithm used in training neural networks. It primarily helps in correcting errors by adjusting the weights of the network based on the difference between the predicted output and the actual output. This process involves calculating the gradient of the loss function with respect to each weight by applying the chain rule, which tells us how to change the weights to minimize the loss.

During backpropagation, the algorithm works backward through the network, layer by layer, updating the weights to reduce the error in predictions. By iteratively adjusting the weights, backpropagation effectively allows the neural network to learn from its mistakes, leading to better accuracy in future predictions.

The other options address different concepts not directly related to what backpropagation achieves. Simply increasing the size of the network doesn't guarantee better performance or error correction. Data normalization and input complexity reduction are preprocessing techniques that can improve model performance but are not the specific purpose of backpropagation. Thus, the correct understanding is that backpropagation is fundamentally about correcting errors and improving the model’s performance through weight adjustments.