What Does Single-Layer Neural Network Mean?
A single-layer neural network represents the most simple form of neural network, in which there is only one layer of input nodes that send weighted inputs to a subsequent layer of receiving nodes, or in some cases, one receiving node. This single-layer design was part of the foundation for systems which have now become much more complex.
Techopedia Explains Single-Layer Neural Network
One of the early examples of a single-layer neural network was called a “perceptron.” The perceptron would return a function based on inputs, again, based on single neurons in the physiology of the human brain. In some senses, perceptron models are much like “logic gates” fulfilling individual functions: A perceptron will either send a signal, or not, based on the weighted inputs. Another type of single-layer neural network is the single-layer binary linear classifier, which can isolate inputs into one of two categories.
