I don't know if this is the type of thing you're looking for, BUT... I made this little web app for visualizing the prediction gradient for a binary classificat

I don't know if this is the type of thing you're looking for, BUT... I made this little web app for visualizing the prediction gradient for a binary classification problem with 2 dimensional inputs [0]. You basically add members of each class by clicking in the plane. It has some bells and whistles for viewing the decision gradient (the probability of any given point in space being of one class or the other) and how this changes after each training. You can can also do things like change the learning rate, edit weight values, and add noise to weights. In this toy problem, you can get more of a "feel" for the complexity capability of a network (by trying different clusterings of classes, etc...). Unfortunately, I hard coded it to have 2 hidden units. In retrospect, it would have been better to make the number of hidden units tunable as well so that one could visualize how a network with more non-linearities can draw increasingly complex decision boundaries. [0] http://www.math.fsu.edu/~mhancock/#!/software/web-apps/neural-net/ http://www.math.fsu.edu/~mhancock/#!/software/web-apps/neura...