Which algorithm is pivotal for adjusting weights and biases in neural networks to minimize errors?

The pivotal algorithm for adjusting weights and biases in neural networks to minimize errors is backpropagation. This algorithm is essential because it enables the neural network to learn from the errors made during training.

Backpropagation works by calculating the gradient of the loss function with respect to each weight by the chain rule, allowing the network to update its weights in a direction that reduces the error in its predictions. It operates in two main phases: the forward pass, where the input data is passed through the network to generate predictions, and the backward pass, where the error is propagated back through the network to compute gradients. These gradients then guide how the weights and biases are adjusted to improve the model's performance.

This adjustment process is integral to the learning procedure of neural networks, making backpropagation a cornerstone of training deep learning models. The other options, although relevant in various contexts of machine learning, do not specifically address the method used for optimizing weights and biases in neural networks.