difference between a cost function and the gradient descent equation in logistic regression

Question

I'm not able to understand the difference between the cost function and the gradient. There are examples on the net where people compute the cost function and then there are places where they don't and just go with the gradient descent function

What is the difference between the two if any?

Answer 1

Cost function is a way to evaluate the performance of the model/algorithm. So if the predicted values differ a lot  from the actual values then this cost function will be high. This also indicates that the algorithm is not performing well, or not learning well from the data.
While Gradient descent is used for finding a local minimum, it is an optimization algorithm used to train machine learning models and neural networks.
Gradient descent helps to find the best parameters that minimize the model’s cost function.