Increase the relative error tolerance for the NN_mnist test case on multi-VM to
10%. Note: this is needed because only 1 node contributes (each mode is running
in multi-threaded single-node mode), and doesn't get to see all the training
data. Expect this test to be tightened as soon as the distributed version is
ready.
Use a local single-VM threaded trainer for Mnist example.
Add scatter plot of the two_spiral dataset for illustration.
Speed up NN with tanh by not calling tanh(x), but an identical expression
containing only exp(x).

This speeds up a single-layer NN using tanh by 33%.
Add the "Two Spirals" test for Neural Networks back.  Use parameters that lead
to fast convergence (about 10 seconds on a laptop), and test that we achieve 0%
training error.  Also only run this during nightly testing, not from
JUnitRunner, since we already have the Iris test there.
Change some default arguments for NN to something that might generalize better.
Make test_junit.py run from inside the h2o/py directory.