gradient descent & ascent

stochastic gradient descent is to minimize cost function:

$\theta_j := \theta_j - \alpha \frac{\partial}{\partial \theta_j}J(\theta)$

while gradient ascent is to maximize likelihood function:

$\theta_j := \theta_j + \alpha \frac{\partial}{\partial \theta_j}l(\theta)$