**Understanding the Perceptron Model in a Neural Network**

Looking around the hottest fashion in Artificial Intelligence(AI) and Machine Learning(ML) are inclusively Neural Networks, they are computational algorithms or models designed as per the structure of the human brain.

The most certain property of the neural network is the ability to understand from data, they are information processing structures that have acquired huge achievement in various domains from marketing to research.

There are numerous kinds of neural networks random forest, SVM, LDA, etc from which single and multilayer perceptron learning algorithms have an adequate place. In this post, you will see **the basics of the perceptron model, its working and some essentials of the perceptron model**.

**Introduction**

In the Artificial Neural Network(**ANN**), the perceptron is a convenient model of a biological neuron, it was the early algorithm of binary classifiers in supervised machine learning. The purpose behind the designing of the perceptron model was to incorporate visual inputs, organizing subjects or captions into one of two classes and dividing classes through a line.

Classification is one most important elements of machine learning, especially in image transformation. Machine learning algorithms exploit various means of processing to identify and analyze patterns. Proceed with classification tasks, the perceptron algorithms analyze classes and patterns in order to attain the linear separation between the various class of objects and correspond patterns obtained from numerical or visual input data.

**What is the perceptron model, precisely?**

Talking in reference to the history of the perceptron model, it was first developed at Cornell Aeronautical Laboratory, United States, in 1957 for machine-implemented image recognition. The machine was first ever created artificial neural networks.

At the same time, the perceptron algorithm was expected to be the most notable innovation of artificial intelligence, it was surrounded with high hopes but technical constraints step out the door that turns out with the conclusion that single-layered perceptron model only applicable for the classes which are linearly separable.

Later on, discovered that multi-layered perceptron algorithms enabled us to classify non linearly separable groups.

Till now, you must have got the core idea of studying the perceptron model, let’s move one step closer to target, **kinds of perceptron models**;

- Single-layered perceptron model, and
- Multi-layered perceptron model.

Defining them in deep!!!

**1. Single-layered perceptron model**

A single-layer perceptron model includes a feed-forward network depends on a threshold transfer function in its model. It is the easiest type of artificial neural network that able to analyze only linearly separable objects with binary outcomes(target) i.e. 1, and 0.

If you talk about the functioning of the single-layered perceptron model, its algorithm doesn’t have previous information, so initially, weights are allocated inconstantly, then the algorithm adds up all the weighted inputs, if the added value is more than some pre-determined value( or, threshold value) then single-layered perceptron is stated as activated and delivered output as +1.

In simple words, multiple input values feed up to the perceptron model, model executes with input values, and if the estimated value is the same as the required output, then the model performance is found out to be satisfied, therefore weights demand no changes. In fact, if the model doesn’t meet the required result then few changes are made up in weights to minimize errors.

**2. Multi-layered perceptron model**

A multi-layered perceptron model has a structure similar to a single-layered perceptron model with more number of hidden layers. It is also termed as a **Backpropagation algorithm**. It executes in two stages; the **forward stage** and the **backward stages**.

In the forward stage, activation functions are originated from the input layer to the output layer, and in the backward stage, the error between the actual observed value and demanded given value is originated backward in the output layer for modifying weights and bias values.

In simple terms, multi-layered perceptron can be treated as a network of numerous artificial neurons overhead varied layers, the activation function is no longer linear, instead, non-linear activation functions such as Sigmoid functions, TanH, ReLU activation Functions, etc are deployed for execution.

**Conclusion**

In the light of this blog, you have learned that the perceptron models are the more simplistic kind of neural network in which they carry an input, the weight of each input, take the sum of weighted input and an activation function is applied.

They accept and construct only binary values,i.e, perceptrons only implement for binary classification with one limitation that they are only applicable for linearly separable objects.

Perceptrons are the fundamental of the neural network, one should have a good understanding of the perceptron model that furnishes advantages when studying deep neural networks.