human
I am working on the file "2.Backpropagation.ipynb".
The first task is:
We know one way of optimising algorithms in front of large scale data sets, i.e. stochastic subgradient methods. In short this is an iterative algorithm that updates the unknown variables proportional to minus the gradient magnitude at each iteration. 
Let us fill in the gaps for a classical linear regression where the model is $f(x,\omega) = \omega^Tx$,
Can you write Python code for it?

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Write me a the Python code you requested?
assistant
#Here is the Python code you requested.

import numpy as np
class model:
    def __init__(self,w):
        self.w = w
    def forward(self,x):
        #Takes a data point and evaluates f(x,w)
        return np.dot(self.w,x.T)