human
I am working on the file "FADL1/L7CA_lesson7-cifar10.ipynb".
The first task is:
And now this model is going to train beautifully just because of that one line: def forward(self, x): return x + super().forward(x). Why is that?
This is called a ResNet block. It says its prediction $$y = x + f(x)$$ in this case the function is a convolution. Which is also saying: $$f(x) = y - x$$, where f(x) is the current layer's prediction, y is the prediction from the previous layer. What it's doing is trying to fit a function f to the difference between y and x. That difference is the residual. 
If y is what I'm trying to calculate, and x is the thing I've most recently calculated (input to current layer), then the difference between the two is essentially the error ito what I've calc'd so far. So this is saying  attempt to find a set of convolutional weights that attempts to fill in the amount I was off by.
Lecture at ~ 1:55:00
Can you write Python code for it?

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Write me a Python code to find a set of convolutional weights that attempts to fill in the amount I was off by?
assistant
#Here is Python code to find a set of convolutional weights that attempts to fill in the amount I was off by.

learn = ConvLearner.from_model_data(Resnet([10,20,40,80,160], 10), data)

wd=1e-5

%time learn.fit(1e-2, 2, wds=wd)

%time learn.fit(1e-2, 3, cycle_len=1, cycle_mult=2, wds=wd)

%time learn.fit(1e-2, 8, cycle_len=4, wds=wd)