The digit recognition MNIST dataset was developed by Yann LeCun, Corinna Cortes, and Christopher Burges for assessing machine learning models on the handwritten digit problem. Digit images were taken from a mixture of scanned documents, normalized in size, and centered. Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel value is an integer between 0 and 255, inclusive. We develop a digit recognition pipeline. We have 10 digits (0 to 9), or 10 classes, to predict.
Digit recognition
Getting ready
In this recipe, we develop a modeling...
