In this chapter, we will build a system that will take an image as an input and give a prediction on what the object in it is. We will take on the role of a vision system for a car, looking around at any obstacles in the way or on the side of the road. Images are of the following form:

This dataset comes from a popular dataset called CIFAR-10. It contains 60,000 images that are 32 pixels wide and 32 pixels high, with each pixel having a red-green-blue (RGB) value. The dataset is already split into training and testing, although we will not use the testing dataset until after we complete our training.

Opening a new IPython Notebook, we can see what the data looks like. First, we set up the data filenames. We will only worry about the first batch to start with, and scale up to the full dataset...