9.2 THE NEURAL NETWORK STRUCTURE
Let us examine the simple neural network shown in Figure 9.2.
![Schematic displaying the simple example of a neural network starting from the input to hidden leading to output layer represented by ovals labeled node 1, 2, and 3 to node A and B leading to node Z.](https://static.packt-cdn.com/products/9781119526810/graphics/images/c09f002.gif)
Figure 9.2 Simple example of a neural network.
A neural network consists of a layered, feedforward, completely connected network of artificial neurons or nodes.
- The feedforward nature of the network restricts the network to a single direction of flow and does not allow looping or cycling.
- Most networks consist of three layers: an input layer, a hidden layer, and an output layer.
- There may be more than one hidden layer, although most networks contain only one, which is sufficient for most purposes.
- The neural network is completely connected, meaning that every node in a given layer is connected to every node in adjoining layers, although not to other nodes in the same layer.
- Each connection between nodes has a weight (e.g. W1A) associated with it.
- At initialization, these weights are randomly assigned to values between 0 and 1.
The number of input nodes depends on the number and type...