There are numerous ways to set up a neural network, and it can be difficult to figure out what combination of settings and architectures will get the best results. We’ll investigate a few different typical network topologies including adding more “depth” and “width”, and evaluate what network topology is best for our data set. For example, you may want a very deep network for increased accuracy on very complex problems, but the training time will take longer. Or, you may add width to your network to increase accuracy, but this has a risk of overfitting.