The perceptron is an artificial neuron or a neural network unit that performs certain calculations to detect input data capabilities or business intelligence. In 1957, Frank Rosenblatt invented Perceptron.

The study rule of the Perceptron network based upon the proposed original MCP neuron.

A perceptron is a supervised binary classification learning algorithm. This algorithm allows neurons to learn elements and processes them one by one during preparation.

There are two kinds of perceptions that exist and those are :

  • Single-layer
  • Multilayer

Perceptron has only two input and output layers in a single layer. There is just one layer, thus the limitations of single-layer perceptron exist. The hidden layers are not used as a multilayer perceptron. Elemental nodes in the next layer are connected to the node or several nodes. The next layer of a node takes a weighted average of all its inputs

A multilayer perceptron algorithm is a kind of artificial neural feed network that produces a series of input outputs. An MLP is a neural network that interconnects many layers in a directional graph, such that the signal path in the nodes only takes one direction.  As a multilayer perceptron example, Input and output layers are part of the MLP network. More computing power is provided to multilayer perceptrons neural networks of two or more layers

In order to draw a linear decision boundary, the Perceptron algorithm learns the weights for entrance signals.

This allows you to differentiate between single-layer and multilayer perceptron as the two linear groups 1 and -1.

The perceptron classifier is made up of four components.

  • Values of input or a layer of input
  • Net amount 
  • Weights and Bias
  • Activation function

The perceptron neural network functions the same as the sensor. Therefore, learn how the perceptron functions if you wish to see how the neural network works.

In this article let us look at:

  1. How does it work?
  2. Why do we need Weights and Bias?
  3. Where we use Perceptron?

1. How does it work?

Let us see the example with these simple steps work on the perception model

a. Any input x is compounded by its weights w. Let’s call it k. Let’s call it k.

b. Add and call all multiple values Weighted Sum.

c. Upon proper activation function, apply for the weighted number.

Assume that we have one neuron and three inputs x1, x2, x3 multiplied respectively with the weights w1, w2, w3.

The concept is simple, since there is a function within the neuron, given the numeric value of the inputs and weights that produces an output.

The weighted sum is the applications of the perceptron function since it is the sum of the weights and the inputs. What if we want the outputs to fall within a certain range say 0 to 1, then it seems a reasonable function.

This can be done by the use of a mechanism called Activation. A feature activation transforms the given input (in this case, the weighted amount will be the input) to a certain input depending on a number of laws.

There are several types of activation functions, such as:

Hyperbolic Tangent: used for a number between -1 and 1.

Logistic function: for the contribution of 0 to 1.

2. Why do we need Weights and Bias?

  • Weights

Weights are the coefficients of the equation to be solved. Negative weights lower the output value. If a neural network is trained on the training set, a number of weights are initialised. These weights are optimised and the optimal weights are generated during the training phase. The weighted number of inputs is first calculated by a neuron. 

  • Bias 

Bias is just a constant value (or a constant vector) applied to the input and weight product. For the offset of the result, bias is used. Let us presume that if your input is 0, you want your neural network to return 2. As the sum of the weight and input product is 0, how are you going to ensure that the network neuron returns 2?  A 2 prejudice can be added.

If we do not have partiality, the neural network actually multiplies the inputs and weights by the matrix. The data collection can be quickly over-adapted. Added bias decreases the uncertainty and thus makes the neural network more flexible and more general. Bias is simply the negative of the threshold and thus controls the bias value when the activation mechanism is used. The partiality is used to move the activation response to the positive or negative side.

3. Where we use Perceptron?

The building blocks of neural networks are perceptrons. Perceptron is normally used in two sections for the classification of the data. Therefore, it is known as the Binary Linear Classifier.


This article explained perceptron example step by step, how does it works, weights and bias are and how they can help a neural network perform better forecasts.

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