Add a learning rate decay factor to have a drop off in the initial learning
rate from one layer to the next. This allows the learning rate of the output
layer to be slightly less than the learning rate in the first few hidden
layers, which can help since gradients are typically higher in the last few
layers.
Remove NeuralNetScore class (behave just like GBM, DRF and GLM2).
Add logStart() to NN.
Use +Inf for initial value of max_w2, and add a (predictable) branch to avoid a few extra flops.
Change the description of the learning rate decay factor.
Disable NN regression for now. Use the new encoding for missing values in scoring.
Merge branch 'arno-deeplearning'
Revert "Remove NeuralNetScore class (behave just like GBM, DRF and GLM2)."

This reverts commit 2aa95b81a8036dae6bb42c8ac4f23cbc24492c40.
Merge branch 'arno-deeplearning'