Nesterov momentum implementation

Based off of the [simplification suggested by Ian Goodfellow](https://github.com/lisa-lab/pylearn2/issues/677) (@goodfeli) and [the corresponding paper by Yoshua Bengio](http://arxiv.org/pdf/1212.0901v2.pdf).

Still need to add tests to the test suite, but I'm looking for a few more eyes on the code to make sure the basics check out. 
First, the velocity update rule, copied from the paper:
![eqn](http://latex.codecogs.com/png.latex?%5Cdpi%7B120%7D%20%5Clarge%20%5C%5C%20v_t%20%3D%20%5Cmu_%7Bt-1%7Dv_%7Bt-1%7D%20-%20%5Cepsilon_%7Bt-1%7D%20%5Ctriangledown_f%28%5CTheta_%7Bt-1%7D%29)
Translated into code, this becomes:
```python
updates[vel] = self.momentum * vel - scaled_lr * grad
```

And then the parameter update rule from the paper:
![eqn2](http://latex.codecogs.com/png.latex?%5Cdpi%7B120%7D%20%5Clarge%20%5C%5C%20%5CTheta_t%20%3D%20%5CTheta_%7Bt-1%7D%20-%20%5Cmu_%7Bt-1%7D%20v_%7Bt-1%7D%20&plus;%20%5Cmu_t%20v_t%20&plus;%20v_t)

With a bit of reorganization:
![eqn3](http://latex.codecogs.com/png.latex?%5Cdpi%7B120%7D%20%5Clarge%20%5C%5C%20%5CTheta_t%20%3D%20%5CTheta_%7Bt-1%7D%20&plus;%20%5Cmu_t%20v_t%20&plus;%20v_t%20-%20%5Cmu_%7Bt-1%7D%20v_%7Bt-1%7D)

We can then substitute in the first equation and simplify to arrive at the update rule I used.

![eqn5](http://latex.codecogs.com/png.latex?%5Cdpi%7B120%7D%20%5Clarge%20%5C%5C%20%5CTheta_t%20%3D%20%5CTheta_%7Bt-1%7D%20&plus;%20%5Cmu_t%20v_t%20&plus;%20%28%20%5Cmu_%7Bt-1%7Dv_%7Bt-1%7D%20-%20%5Cepsilon_%7Bt-1%7D%20%5Ctriangledown_f%28%5CTheta_%7Bt-1%7D%29%20%29%20-%20%5Cmu_%7Bt-1%7D%20v_%7Bt-1%7D)
![eqn6](http://latex.codecogs.com/png.latex?%5Cdpi%7B120%7D%20%5Clarge%20%5C%5C%20%5CTheta_t%20%3D%20%5CTheta_%7Bt-1%7D%20&plus;%20%5Cmu_t%20v_t%20-%20%5Cepsilon_%7Bt-1%7D%20%5Ctriangledown_f%28%5CTheta_%7Bt-1%7D%29)

Or, expressed in code:
```python
inc = self.momentum * updates[vel] - scaled_lr * grad
updates[param] = param + inc
```