Deep-Learning-with-PyTorch

Using a neural
network to fit the data
This chapter covers
• Nonlinear activation functions as the key
difference compared with linear models
• Working with PyTorch’s nn module
• Solving a linear-fit problem with a neural network
So far, we’ve taken a close look at how a linear model can learn and how to make
that happen in PyTorch. We’ve focused on a very simple regression problem that
used a linear model with only one input and one output. Such a simple example
allowed us to dissect the mechanics of a model that learns, without getting overly
distracted by the implementation of the model itself. As we saw in the overview diagram
in chapter 5, figure 5.2 (repeated here as figure 6.1), the exact details of a
model are not needed to understand the high-level process that trains the model.
Backpropagating errors to parameters and then updating those parameters by taking
the gradient with respect to the loss is the same no matter what the underlying
model is.
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