\item \subquestionpoints{7} For a training set
$\{(x^{(i)}, y^{(i)});\, i=1,\ldots,\nexp\}$, let the log-likelihood of an example
be $\log p(y^{(i)} | x^{(i)}; \theta)$. By taking the derivative of the
log-likelihood with respect to $\theta_j$, derive the stochastic gradient
ascent update rule for learning using a GLM model with Poisson responses $y$
and the canonical response function.