Introduction to Perceptron Model in Machine Learning

What is the Perceptron Model?

 

A perceptron model, in Machine Learning, is a supervised learning algorithm of binary classifiers. A single neuron, the perceptron model detects whether any function is an input or not and classifies them in either of the classes. 

 

Representing a biological neuron in the human brain, the perceptron model or simply a perceptron acts as an artificial neuron that performs human-like brain functions. A linear ML algorithm, the perceptron conducts binary classification or two-class categorization and enables neurons to learn and register information procured from the inputs. 

 

This model uses a hyperplane line that classifies two inputs and classifies them on the basis of the 2 classes that a machine learns, thus implying that the perceptron model is a linear classification model.  Invented by Frank Rosenblatt in 1957, the perceptron model is a vital element of Machine Learning as ML is recognized for its classification purposes and mechanism. 

 

There are 4 constituents of a perceptron model. They are as follows-

1. Input values 

2. Weights and bias 

3. Net sum 

4. Activation function

 

The perceptron model enables machines to automatically learn coefficients of weight which helps in classifying the inputs. Also recognized as the Linear Binary Classifier, the perceptron model is extremely efficient and helpful in arranging the input data and classifying the same in different classes. 

 

Understanding the Perceptron

 

As we have already learned about the perceptron model in Machine Learning, we will now understand the model in a bit more detail. Let’s get started! 

 

The simplest variant of artificial neuron networks, the perceptron model resembles a biological neuron that simply helps in the linear binary classification with the help of a hyperplane line. 

 

There are 2 types of perceptron models-

 

1. Single Layer Perceptron- The Single Layer perceptron is defined by its ability to linearly classify inputs. This means that this kind of model only utilizes a single hyperplane line and classifies the inputs as per the learned weights beforehand. 

 

2. Multi-Layer Perceptron- The Multi-Layer Perceptron is defined by its ability to use layers while classifying inputs. This type is a high processing algorithm that allows machines to classify inputs using various more than one layer at the same time.  

 

(Must read: Machine learning models)

 

The working of the model is based on the Perceptron Learning Rule that implies that the algorithm is enabled to automatically learn respective coefficients of weights that designate several inputs. 

 

The perceptron model registers inputs with the machine and allots them with certain weights as per the coefficients that lead a particular input into a specific class. This is decided on the basis of the final value derived by calculating the net sum and activation function at the end stages. 

 

Let us now go through a step-by-step procedure in order to understand the way the perceptron model operates.

 

1. Enter bits of information that are supposed to serve as inputs in the first layer (Input Value).

2. All weights (pre-learned coefficients) and input values will be multiplied. The multiplied values of all input values will be added.

3. The bias value will shift to the final stage (activation function/output result).

4. The weighted input will proceed to the stage of the activation function. The bias value will be now added.

5. The value procured will be the output value that will determine if the output will be released or not.  

 

The perceptron algorithm, using the Heaviside activation function is summarised as follows- 

 

f(z) = {1 if xTw+b > 0

       = {0 otherwise    

 

The Input value of the model consists of various artificial neurons in artificial intelligence that facilitate the entry of data into the system or machine. 

 

When the inputs are registered in the machine, the perceptron algorithm primarily applies the already learned value of weight (dimension or strength of the connection between data units). These weights are then multiplied with the input values and headed to the net sum (total value). 

 

Ultimately, the input value proceeds to the activation function where output is released or scrapped out. The activation function (weighted sum total added with bias) in the final stage is important for determining if an input’s value is greater than 0. 

 

The process that enables the perceptron model to conduct mathematical operations for converting input into output is called training. As the process of training is implemented in the working of the perceptron model wherein machines are made fully capable of calculating output values even without being fed with input values. 

 

The process of training involves feeding machines with historic data in order to prepare them for the future and instill predictive patterns. Based on artificial neural networks that tend to imitate the human brain, the perceptron model works along the lines of machine learning as it continuously interprets data and produces qualitative patterns. 

 

“It’s essentially finding patterns in the training data and generating weights that will produce useful output by applying these patterns to new data.”-Perceptron Model  

 

Significance of the Perceptron model 

 

The Perceptron Model in Machine Learning is a supervised learning algorithm that focuses on the linear binary classification of inputs. This algorithm, when it was primarily being worked upon, was intended to facilitate image recognition in machines. 

 

The model was considered a benchmark innovation for the development of Artificial Intelligence technology as it had the capability to strengthen the existing Machine Learning algorithms and even generate more advanced ones. 

 

As it primarily worked and its popularity boosted up in the initial days, a lot of hope and positivity was attached to this innovation. However, it was soon that the infrastructural limitations were exposed and it was thus perceived to be a long way before perceptron could be easily implemented. 

 

A learning algorithm that self-arranges a network of artificial neurons to incorporate desired behaviors, the significance of the perceptron model enables machines to work efficiently in terms of binary classification. 

 

(Must read: Deep learning algorithms)

 

While the single layer perceptrons can only classify inputs into classes with a single layer, the multi-layer perceptron enables the model to conduct a classification of inputs with the help of more than one layer, making it suitable for more advanced and complex inputs. 

 

A supervised learning algorithm, like the perceptron model, is the most sought-after algorithm that prevails in the field of Machine Learning. Prevalent in the field of data analytics, the perceptron model initiates binary classification and leads to problem-solving when it comes to bits of data. 

 

(Referred blog: Types of Machine Learning)

 

Ofcourse all the things that it is capable of doing, the perceptron model is extremely reliable and significant when it comes to Machine Learning and Artificial Intelligence. 

 

 

Future of the Perceptron

 

Machine Learning is a technique of Artificial Intelligence that aims to interpret data by building intuitive patterns and applying these patterns in the future. Without being extensively programmed, artificial intelligence looks for computational intelligence that can be replicated into computers by working along the lines of the human brain. 

 

(Also read- Neuromorphic Computing )

 

When we talk about ANN or artificial neural network in machine learning, AI focuses on complex patterns and learning abilities that can be successfully registered and worked on by computers. 

 

The future of the perceptron model in this scenario is fairly bright and significant. Why? The focus on the perceptron model comes out of its ability to conduct binary classification using a linear method of segregation. 

 

As Artificial Intelligence continues to evolve and develop, the future of perceptron technology will continue to support and facilitate analytical behavior in machines that will, in turn, add to the efficiency of computers. 

 

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What’s more, the cumulative focus on neural networks suggests that the perceptron model will likely become more significant in the technological realm as it will assist machines to predict the future in terms of data and run efficiently without even needing extensive programming. 

 

That said, the perceptron model will become more advanced and will alter the way machines work along the lines of the human brain. 

 

 

Wrapping Up

 

To wrap up, the perceptron model is a supervised learning algorithm in Machine Learning that facilitates linear binary classification. Categorized into two types, the perceptron model can be either a single-layered perceptron or a multi-layered perceptron, varying on the grounds of the number of layers used to classify inputs. 

 

(Must read: Machine Learning Tutorial for Beginners)

 

With numerous elements that enable machines to mathematically calculate an input’s total value, the perceptron involves input values, weights and bias, net sum, and activation function. These elements work on the inputs fed to the machines at different stages and result in the release of an output. 

 

(Recommended blog: What is Automated Machine Learning(AutoML)?)

 

Immensely contributing to the field of Machine Learning, the Perceptron model is likely to become more advanced and will grab the focus of AI as it will enable computers to work more efficiently on complex problem-solving information with the help of artificial neurons.