Focusing on data types is where we will begin the data processing journey. Each step in this process is crucial to understand. Data types refer to the structure in which the data is held in Python. Think of a dictionary, array of floats, and so on. These are the data types that we would like to understand and consider. The example that we are going to explore in this set of recipes is going to be a parsing example for images since the first few recipes in this book will revolve around the usage of two-dimensional imagery. We're going to start with a simple and small dataset called the MNIST dataset. This data is used across all kinds of ML and for good reason. it's a set with 60,000 handwritten images that're labeled and easy to understand. Also, the data is quite small so downloading and manipulating it's fairly simple. For some sample methods, this data will allow us to quickly work and it won't take up a lot of space.

First, we should understand the original intent of the data. Almost every dataset supplied out there with the intent of being use for deep learning will have a description of the data. MNIST has a description and we can review it prior to understanding the data type we'll use for training or manipulation. The basics are located at the following link: http://yann.lecun.com/exdb/mnist/.