**Basics of The Perceptron**

The perceptron(or single-layer perceptron) is the simplest model of a neuron that illustrates how a neural network works. The perceptron is a machine learning algorithm developed in 1957 by Frank Rosenblatt and first implemented in IBM 704.

The perceptron is a network that takes a number of inputs, carries out some processing on those inputs and produces an output as can be shown in Figure 1.

Figure 1: How the Perceptron Works |

*How the Perceptron Works*

How the perceptron works is illustrated in Figure 1. In the example, the perceptron has three inputs *x _{1}, x_{2}* and

*x*and one output.

_{3}The importance of this inputs is determined by the corresponding weights

*w*and

_{1}, w_{2}*w*assigned to this inputs. The output could be a 0 or a 1 depending on the weighted sum of the inputs. Output is 0 if the sum is below certain threshold or 1 if the output is above certain threshold. This threshold could be a real number and a parameter of the neuron. Since the output of the perceptron could be either 0 or 1, this perceptron is an example of binary classifier.

_{3}This is shown below in Equation 1

Equation 1: output of a perceptron |

** The Formula**

Let’s write out the formula that joins the inputs and the weights together to produce the output

*Output*

**=**w_{1}x_{1}+ w_{2}x_{2}+ w_{3}x_{3 }This function is a trivial one, but it remains the basic formula for the perceptron but I want you to read this equation as

*Output ‘depends on’ w*

_{1}x_{1}+ w_{2}x_{2}+ w_{3}x_{3 }The reason for this is because, the output is not necessarily just a sum of these values, it may also depend on a bias that is added to this expression. In other words, we can think of a perceptron as a ‘judge who weights up several evidences together with other rules and the makes a decision’

We would discuss this in detail in the Neural Networks lesson.

This operation of the perceptron serves as the basics of Neural Networks and would serve as a good introduction to learning neural network which we would be examining in subsequent lessons.

Now we would examine a more detailed model of a neural network, but that would be in part 2 because I need to keep this lesson as simple as possible.

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