Which method minimizes a cost function by gradually tuning model parameters?

The method that minimizes a cost function by gradually tuning model parameters is gradient descent. This algorithm works by iteratively adjusting the weights of a model based on the gradient of the loss function with respect to those weights.

In simple terms, gradient descent identifies the direction of steepest descent of the loss function and makes small adjustments in model parameters to reduce the cost. By repeatedly calculating these gradients and updating the parameters, the algorithm converges towards a set of values that minimize the cost function.

This process is fundamental in training machine learning models, particularly in optimization problems where you want to find the best parameters that yield the lowest possible error in predictions. The ability of gradient descent to converge towards a local minimum makes it a valuable technique in various applications, including deep learning.

Other methods mentioned, like Newton's method, may also seek to minimize functions but do so with different approaches, often involving second-order derivatives, which may not always be as computationally efficient as gradient descent. Stochastic gradient ascent is focused on maximizing objectives rather than minimizing them, and genetic algorithms use a biologically-inspired approach to explore solutions rather than fine-tuning parameters incrementally. Each option has its own unique approach to problem-solving, but gradient descent is specifically tailored to the gradual minimization