To build a simple framework into the neural network components and architectures, we will give a simple and straightforward build of the original concepts which paved the way to the current,complexand variedNeural Network landscape.
An artificial neuron is a mathematical function conceived as a model for a real biological neuron.
Its main features are that it receives one or more inputs (training data), and sums them to produce an output. Additionally, the sums are normally weighted (weight and bias), and the sum is passed to a nonlinear function (Activation function or transfer function).
The Perceptron is one of the simplest ways of implementing an artificial neuron and it's an algorithm that dates back from the 1950s, first implemented in the 1960s.
It is basically an algorithm that learns a binary classification function, which maps a real function with a single binary one:
The following image shows a single layer perceptron...