Now that we have completed exploratory data analysis and data preprocessing, let's turn our attention towards designing the neural network architecture. In this project, we will be using MLPs.
An MLP is a class of feedforward neural network, and it distinguishes itself from the single-layer perceptron that we've discussed in Chapter 1, Machine Learning and Neural Networks 101, by having at least one hidden layer, with each layer activated by a non-linear activation function. This multilayer neural network architecture and non-linear activation allows MLPs to produce non-linear decision boundaries, which is crucial in multi-dimensional real-world datasets such as the Pima Indians Diabetes dataset.
