The challenge is to classify a handwritten digit based on a 28-by-28 black and white image. MNIST is often credited as one of the first datasets to prove the effectiveness of neural networks.
In a series of posts, I’ll be training classifiers to recognize digits from images, while using data exploration and visualization to build our intuitions about why each method works or doesn’t. Like most of my posts I’ll be analyzing the data through tidy principles, particularly using the dplyr, tidyr and ggplot2 packages. In this first post we’ll focus on exploratory data analysis, to show how you can better understand your data before you start training classification algorithms or measuring accuracy. This will help when we’re choosing a model or transforming our features.
Read on for the analysis.