Multiple single-level

Multiple single-level is a type of network architecture where multiple single-level networks are “stacked” to form a single multilevel network. This type of architecture gives the whole network the ability to learn and make better predictions due to the increased complexity of having multiple levels of neurons that can be connected to each other. It also allows for more complex functions to be performed and can help reduce the amount of time required to train the network. Think of it like this: by stacking multiple single-level networks together, you are adding more layers to your network that work together to create better, more accurate results.