Here's a visual representation of a neural network:
As you can see from the figure, there are three distinct layers in a neural network: Input layer, Hidden layer (or middle), and Output layer.
There can be more than one hidden layer; however, one hidden layer would be enough to resolve the majority of real-life problems.
How do we determine the network's topology, and how many neurons to create for each layer? Let's make this determination layer by layer.
The input layer defines the number of inputs into the network. For example, let's say you want to create an ANN, which will help you determine what animal you're looking at given a description of its attributes. Let's fix these attributes to weight, length, and teeth. That's a set of three attributes; our network will need to contain three input nodes.