The ______________ technique is used to adjust the weights in a neural network to minimize the cost function.

The technique used to adjust the weights in a neural network to minimize the cost function is called gradient descent.

Gradient descent is an optimization algorithm that iteratively adjusts the weights of the neural network based on the gradient of the cost function with respect to those weights. The basic idea is to calculate the gradient (or slope) of the cost function and then move the weights in the opposite direction of the gradient to reduce the cost. This process continues until the cost function is minimized, or the changes in weights become very small.

There are various forms of gradient descent, including stochastic gradient descent (SGD), mini-batch gradient descent, and batch gradient descent, each with its own advantages depending on the application and dataset size.