SOMs, or Self-Organizing Maps, work by reducing the number of random variables in a model by using unsupervised data. As each neuron connects to its inlet and outlet nodes, the result dimensionality is set as a two-dimensional model in this kind of deep learning technique. The SOM adjusts the value of the nearest nodes or Best Matching Units because each data point bids for its model representation (BMUs). The weights' value varies depending on how close a BMU is. Because weights are considered node characteristics in and of itself, the value signifies the node's position in the network.