RMSProp implementation modeled off of AdaDelta
Add running averages as members of RMSProp class

This enables us to use them as monitored values.
Reorder things in SGD.setup, so we call learning_rule.get_updates
before learning_rule.add_monitoring_channels.
Also add a little bit of doc.
Docs, error/exception updates, and addition of SGD._setup_monitor
Simplified DefaultViewConverter's conversion methods. They now return lightweight views rather than copying data whenever possible.
bugfixes
bugfix; it turned out that DenseDesignMatrix.shape should always be in (0, 1, 'c') order
deprecated zca_dataset one_hot
before fixing DefaultViewConverter to assume a ('b', 'c', 0, 1) memory layout order in desicn matrices
tests pass. Now remove debug printfs.
tests pass. Now remove debug printfs.
commit before merging in master
Merge branch 'master' into efficient_view_converter
addressed Madison and Ian's PR comments, except for testing this PR's impact on the performance of runing cifar10.yaml (it crashed on the master branch)
Learning rule doc fix + PEP8 for learning_rule tests
Added documentation for mlp.Softmax and fixed and tested the get and set weights.
Merge pull request #1077 from JesseLivezey/softmax_weights_topo_view

Added documentation for mlp.Softmax and fixed and tested the get and set...
Merge branch 'master' into efficient_view_converter
Merge pull request #1075 from memimo/zca_ds

deprecated zca_dataset one_hot
Merge pull request #1026 from madisonmay/master

RMSProp learning rule implementation
Nesterov momentum implementation