The human brain is a complex and intriguing organ. Its capabilities go far beyond what meets the eye. Complex physiological, psychological and emotional functions form just the tip of the iceberg when it comes to what the human brain is capable of. It is this very fascinating nature of it that inspires science.

Human beings have an extraordinary tendency to replicate nature. We saw birds flying, and we wanted to have flying objects of our own. Airplanes, which were first such objects that could fly, were a direct result of that observation and the willingness to replicate what we saw and found worthy. Nature is at the center of every such innovation.

Science has forged through all limitations and has tried to replicate the human brain. Much research has gone into understanding how the human brain functions and how easily it holds, interprets and manages so much information. The concept of artificial neural networks draws inspiration from and is found to be a small but accurate representation of the biological neural networks of our brain.

We now have machines that replicate the working of a brain – at least of a few functions. Artificial intelligence has given us machines that could classify objects, communicate with us, foresee future and play games better than us.

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Table of Contents

**What is a neural network?**

A neural network is formed when a collection of nodes or neurons are interlinked through synaptic connections. There are three layers in every artificial neural network – input layer, hidden layer, and output layer. The input layer that is formed from a collection of several nodes or neurons receives inputs. Every neuron in the network has a function, and every connection has a weight value associated with it. Inputs then move from the input layer to layer made from a separate set of neurons – the hidden layer. The output layer gives the final outputs.

**What is the learning algorithm?**

It is an adaptive method that self-arranges a network of computing units to implement the required behavior. Some of these algorithms do this by bringing in front of the network a few examples of the required input-output mapping. An iteration of the correction step is continuously executed until the network produces the required response. A learning algorithm can also be referred to as a closed-loop that features corrections and examples brought forward to the network.

**What is the perceptron learning algorithm?**

A perceptron, a neuron’s computational prototype, is categorized as the simplest form of a neural network. Frank Rosenblatt invented the perceptron at the Cornell Aeronautical Laboratory in 1957. A perceptron has one or more than one inputs, a process, and only one output.

The concept of perceptron has a critical role in machine learning. It is used as an algorithm or a linear classifier to facilitate supervised learning of binary classifiers. Supervised learning is amongst the most researched of learning problems. A supervised learning sample always consists of an input and a correct/explicit output. The objective of this learning problem is to use data with correct labels for making predictions on future data, for training a model. Some of the common problems of supervised learning include classification to predict class labels.

A linear classifier that the perceptron is categorized as is a classification algorithm, which relies on a linear predictor function to make predictions. Its predictions are based on a combination that includes weights and feature vector. The linear classifier suggests two categories for the classification of training data. This means, if classification is done for two categories, then the entire training data will fall under these two categories.

The perceptron algorithm, in its most basic form, finds its use in the binary classification of data. Perceptron takes its name from the basic unit of a neuron, which also goes by the same name.

In some scenarios and machine learning problems, the perceptron learning algorithm can be found out, if you like. It could show limitations that you never knew existed. But then, this is the problem with most, if not all, learning algorithms. They are ideal for some problems, not so for others. At one point, the perceptron networks were also found to be not capable enough of implementing some basic functions. However, this problem was dealt with as soon as multi-layer perceptron networks and improved learning rules came into the picture.

Perceptron today has become an important learning algorithm in the world of artificial intelligence and machine learning. It is considered a reliable and fast solution for the category of problems it has the capabilities of solving. Also, if you develop an understanding of how the perceptron works, you will find the job of understanding more complex networks a lot easier.

**What are the primary components of a perceptron?**

- Input: Features are taken as inputs in the perceptron algorithm. Inputs are denoted as x1, x2, x3, x4, .xn – ‘x’ in these inputs indicates the feature value and ‘n’ the total occurrences of these features. There is also a special input type, which is called bias. We will define the bias a little later.
- Weights: These are values that are calculated during the training of the model. The weights are given an initial value at the start. With every occurrence of a training error, the values of weights are updated. Weights are represented as w1, w2, w3, w4, ..wn.
- Bias: As we alluded to earlier, bias is a special input type. It allows the classifier to move the decision boundary around from its original position to the right, left, up, or down. In terms of algebra, the bias allows the classifier to turn its decision boundary around. The objective of the bias is to shift each point in a particular direction for a specified distance. Bias allows for higher quality and faster model training. Perceptron algorithms can be categorized into single-layer and multi-layer perceptrons. The single-layer type organizes neurons in a single layer while the multi-layer type arranges neurons in multiple layers. In the multi-layer scenario, each neuron of the first layer takes inputs and gives a response to the group of neurons present in the second layer. This process continues until the last layer is reached.
- Activation/step function: Activation or step functions are used to create non-linear neural networks. These functions can change the value of neural networks to 0 or 1. The conversion of value is done to make a data set easy to classify. We can use the step function depending on the value required. Sigmoid function and sign functions can be used for values between 0 and 1 and 1 and -1, respectively. The sign function is a hyperbolic tangent function that is ideal for multi-layer neural networks. Rectified linear unit (ReLu) is another step function that can be used for values approaching zero – value more less than or more than zero. However, linear classification requires the perceptron to be linear.
- Weighted summation: The multiplication of every feature or input value (xn) associated with corresponding weight values (wn) gives us a sum of values that are called weighted summation. Weighted summation is represented as ∑wixi for all i -> [1 to n].

**Steps to perform a perceptron learning algorithm**

- Feed the features of the model that is required to be trained as input in the first layer.
- All weights and inputs will be multiplied – the multiplied result of each weight and input will be added up
- The Bias value will be added to shift the output function
- This value will be presented to the activation function (the type of activation function will depend on the need)
- The value received after the last step is the output value.

**If there are other classification algorithms, such as KNN that we can use in these learning problems, then why perceptron learning algorithm?**

Perceptron algorithm is best suited for problems that are dealing with complex data sets like in image recognition. It is a tough job training the algorithm with KNN and other general classification methods in these cases. Multi-layer perceptrons are ideal for problems with complex data sets. The activation function is a critical component in the perceptron learning algorithm. We can use different activation functions if the learning rate is slow.

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## What do you understand by perceptron learning algorithm?

The simplest kind of a neural network is a perceptron, which is the computational prototype of a neuron. In machine learning, the concept of a perceptron is crucial. It's used as a method or a linear classifier to help binary classifiers learn supervised. One of the most explored learning issues is supervised learning. An input and an appropriate output are always present in a supervised learning sample. The goal of this learning challenge is to use information with correct labels to train a model and make predictions on future data. Classification to estimate class labels is one of the most common supervised learning issues.

## How to perform a perceptron learning algorithm?

In the first layer, provide the parameters of the model that must be trained as input. All values and inputs will be multiplied, and the sum of all multiplied weights and inputs will be calculated. To shift the output function, the Bias value will be added. The activation function will be displayed with this value (the activation function type can be chosen based on the need). The output value is the value obtained after the last step.

## What do you mean by a neural network?

When a group of nodes or neurons are joined together by synaptic connections, a neural network is established. Every artificial neural network has three layers: an input layer, a hidden layer, and an output layer. Inputs are received by the input layer, which is made up of multiple nodes or neurons. Every connection in the network has a weight value, as well as every neuron in the network has a purpose. Inputs then pass from the input layer to the hidden layer, which is made up of a different group of neurons. The final outputs are provided by the output layer.