title: Can someone explain to me the difference between a cost function and the gradient descent equation in logistic regression?
body: <p>I'm going through the ML Class on Coursera on Logistic Regression and also the Manning Book Machine Learning in Action. I'm trying to learn by implementing everything in Python. </p>
  
  <p>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 <code>w :=w - (alpha) * (delta)w * f(w)</code>.</p>
  
  <p>What is the difference between the two if any?</p>