During an exercise of a machine learning course, the task was to use logistic regression and neural networks to recognize handwirtten digits. For this, we are given a dataset containing 5000 training examples of handwritten digits where each example is a 20 x 20 pixel grayscale image of the digit. (I am not looking the answer on to how to do it)
When loading the data, it loads a 5000x400 matrix X where every row is a training example.
I would like to know how they have "unrolled" (as they call it) the 20x20 grid of pixels into a 400-dimensional vector and created this 5000x400 matrix X.
Thank you for yout help!