Notice how we achieved 86% accuracy. This is a great result for two hours of work, but we can do much better. Much of the future potential is in changing the neural network. Our preceding application used a fully-connected setup, where each node on a layer is connected to each node on the previous layer and looks like this:

As you will learn with more complex network setups in coming chapters, this setup is fast but not ideal. The biggest issue is the large number of parameters, which can cause overfitting of the model on the training data.