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A multi-layer neural network contains more than one layer of artificial neurons or nodes. They differ widely in design. It is important to note that while single-layer neural networks were useful early in the evolution of AI, the vast majority of networks used today have a multi-layer model.
Multi-layer neural networks can be set up in numerous ways. Typically, they have at least one input layer, which sends weighted inputs to a series of hidden layers, and an output layer at the end. These more sophisticated setups are also associated with nonlinear builds using sigmoids and other functions to direct the firing or activation of artificial neurons. While some of these systems may be built physically, with physical materials, most are created with software functions that model neural activity.
Convolutional neural networks (CNNs), so useful for image processing and computer vision, as well as recurrent neural networks, deep networks and deep belief systems are all examples of multi-layer neural networks. CNNs, for example, can have dozens of layers that work sequentially on an image. All of this is central to understanding how modern neural networks function.