What optimization algorithm involves adjusting model parameters by following the steepest decrease in the cost function?

The chosen answer is accurate because gradient descent is indeed the optimization algorithm that focuses on minimizing the cost function by adjusting model parameters in the direction of the steepest decrease. In gradient descent, the gradient (or first derivative) of the cost function is computed, indicating the direction in which the cost function increases most rapidly. By moving in the opposite direction of this gradient, the parameters are updated iteratively to reduce the cost.

Gradient descent is widely used in machine learning and optimization because it provides a straightforward method to find the minimum of a function. The method's effectiveness relies on the assumption that the cost function is differentiable and behaves well enough for small changes in parameters to provide a meaningful decrease in cost.

The other options represent different methods or concepts that do not specifically describe adjusting parameters by following the steepest decrease in the cost function. Stochastic gradient ascent, while related, involves maximizing a function rather than minimizing it, and typically applies to scenarios where the individual data points are used to update parameters. Backpropagation is an algorithm used to compute gradients in neural networks but does not itself represent a standalone optimization technique. Newton's method uses second-order information to find optima but does not explicitly follow the steepest descent as gradient descent does.