The network then adjusts its weighted associations according to a learning rule and using this error value. The training of a neural network from a given example is usually conducted by determining the difference between the processed output of the network (often a prediction) and a target output. Neural networks learn (or are trained) by processing examples, each of which contains a known "input" and "result", forming probability-weighted associations between the two, which are stored within the data structure of the net itself. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times. Different layers may perform different transformations on their inputs. Typically, neurons are aggregated into layers. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. The weight increases or decreases the strength of the signal at a connection. Neurons and edges typically have a weight that adjusts as learning proceeds. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. An artificial neuron receives signals then processes them and can signal neurons connected to it. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. Īn ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Artificial neural networks ( ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains.
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