Let's Play with Neural Network - Part 2

While the basic coding for neural network(NN) is ready, we could try to build a simple hypothesis for multi-class classification, a recognizer for classifying hand-written digits is a good topic for practicing. Let's kick off this demo from "Getting Started Project: Digit Recognizer" on the kaggle community. Our goal is clear: giving a grayscale image with 28x28 pixels, identify the digit the picture shows from '0' to '9'. Concretely, our raw features are the grayscale values at every pixel, with those values, NN attempts to figure out the relationship between pixels, and learning the stroke patterns from different digits, via the supervised learning process, NN could find out the most probable digit the pixels formed. These could be summarized in two facts: (1) the size of the feature space(input layer size) is equal to 784 since all grayscale values may vary independently, (2) we need 10 labels(output layer size) to indicate the digits from '0' to '9'.