Handwritten digit recognition is one of the classic tasks undertaken by students when learning the basics of Neural Networks and Computer Vision. The basic idea is to take a number of labeled images of handwritten digits and use those to train a neural network that is able to classify new unlabeled images. For this demo, we show how to use data stored in a large-scale database as our training data. We also explain how to use that same database as a basic model registry. This addition can enable model serving as well as potentially future retraining.
Introduction
MNIST is a set of datasets that share a particular format useful for educating students about neural networks while presenting them with diverse problems. The MNIST datasets for this demo are a collection of 28 by 28-pixel grayscale images as data and classifications 0-9 as potential labels. This demo works with the original MNIST handwritten digits dataset as well as the MNIST fashion dataset.