Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Equation 3.1 - A linear regression model with two features
There is one obvious problem with the model above: Our labels (y) are discrete;
that is, they are either zero or one; no other value is allowed. We need to change
the model slightly to adapt it to our purposes.
"What if we assign the positive outputs to one and the negative
outputs to zero?"
Makes sense, right? We’re already calling them positive and negative classes
anyway; why not put their names to good use? Our model would look like this:
Equation 3.2 - Mapping a linear regression model to discrete labels
Logits
To make our lives easier, let’s give the right-hand side of the equation above a
name: logit (z).
Equation 3.3 - Computing logits
The equation above is strikingly similar to the original linear regression model, but
we’re calling the resulting value z, or logit, instead of y, or label.
"Does it mean a logit is the same as linear regression?"
Not quite—there is one fundamental difference between them: There is no error
term (epsilon) in Equation 3.3.
"If there is no error term, where does the uncertainty come from?"
I am glad you asked :-) That’s the role of the probability: Instead of assigning a data
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