sidereal

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

Bernoulli distribution:$y \epsilon \{0,1\}$, $\phi=p(y=1)$$p(y;\phi)=\phi ^y(1-\phi)^{1-y}$the mean of the Bernoulli is given by $\phi$ Read More