Neural networks need to map inputs to outputs. It seems simple enough, but in most useful cases this means building a network with millions of parameters, which look at millions or billions of relationships hidden in the input data. Do we need all of these relationships? Is all of this information necessary? Probably not. That's where neural network pooling layers can help. In this post, we're going to dive into the deep end and learn how pooling layers can reduce the size of your network while producing highly accurate models.