Okay, so it works by minimizing (equiv. maximizing) some function. But that doesn't say much about how it "learns" the gradient. What function does it care about? Average squared error (predict_prob-Z_i)^2 ? Average absolute error? The likelihood function of some assumed distribution? Maximum distance between the classification border and closest observed points? If I saw someone carrying a bag full of blueberries and some bread home from the grocery store and asked to know how they chose to buy that, to which they replied "I had a list of characteristics which I thought where important for groceries to have in this trip to the store. For each grocery item, I recorded a vector of degrees to which the item possesses each of those characteristics. Finally, I chose the group of groceries that had the best combination of degree vectors", I still wouldn't really know anything about why they bought the blueberries and bread.
The function it minimizes is called the "loss function", and its value for the training and test sets are shown in the upper right area. AFAICT the site doesn't say how it's computed, but I think it's average squared error. The gradient is not learned; if you think of the loss function as a real-valued function of the weights, the gradient is just the partial derivatives with respect to the weights.
