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

"How is this different from the datasets we worked with in previous
chapters?"
It isn’t! Before, our data points were tensors with one or two elements in them;
that is, one or two features. Now, our data points are tensors with 25 elements in
them, each corresponding to a pixel / channel in the original image, as if they were
25 "features."
And, since it is not different, apart from the number of features, we can start from
what we already know about defining a model to handle a binary classification task.
Shallow Model
Guess what? It is a logistic regression!
Equation 4.1 - Logistic regression
Given 25 features, x 0 through x 24 , each corresponding to the value of a pixel in a
given channel, the model will fit a linear regression such that its outputs are logits
(z), which are converted into probabilities using a sigmoid function.
"Oh no, not this again … where are the deep models?"
Don’t worry, this section was named "Shallow Model" for a reason—in the next one,
we’ll build a deeper model with a hidden layer in it—finally!
298 | Chapter 4: Classifying Images