Our Own MNIST Library

Goal of the lesson

In the previous lessons, we built a binary classifier. Now we want to apply that program to MNIST.

The first step is to reshape MNIST’s images and labels into an input for our program. Let’s see how to do that.

Prepare the input matrices

Our binary classifier program expects its input formatted as two matrices: a set of examples $X$ and a set of labels $Y$. Let’s start with the matrix of examples $X$.

$X$ is supposed to have one line per example and one column per input variable, plus a bias column full of 1s. (Remember ...