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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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.
Application of Neural Networks:
Neural Networks are part of Artificial Intelligence, and hence, the application is related to systems that try to resemble the human approach to doing things. Several contemporary application areas of neural networks are:
Computer Vision: No program can be created to let the PC perceive every item in its presence. So, the main route is to use neural systems with the objective that as time passes, the PC can independently perceive new things depending on what it has already perceived.
Natural Language Processing: Neural networks are used in a system that lets the computer identify spoken human language by learning and listening gradually with time.
Pattern Recognition/Matching: It can be implemented in discovering a warehouse of pictures to express that a face coordinates with a known face. It is used in criminal examinations.
The later sections discuss more on perceptron in machine learning.
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].
- Neurons: A neural network is composed of a collection of odes or units known as neurons.
- Synapse: The getting neuron can obtain the sign, process the same, and sign the subsequent one. A neuron can send information or signals through the synapse to another adjacent neuron. It processes it and signals the subsequent one. This process in the perceptron algorithm continues until an output signal is generated.
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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.
How does Perceptron work?
Perceptron is regarded as a single-layer neural network comprising four key parameters in Machine Learning. These parameters of the perceptron algorithm are input values (Input nodes), net sum, weights and Bias, and an activation function. The perceptron model starts by multiplying every input value and its weights. Subsequently, it adds these values to generate the weighted sum. This weighted sum is then applied to the activation function “f” to get the anticipated output. The corresponding activation function is also called the step function. After the perceptron algorithm explained, let’s go through the types of Perceptron Models.
Types of Perceptron Models
Depending on the layers, Perceptron models are categorized into two types as below:
- Single-layer Perceptron Model
- Multi-layer Perceptron model
- Single-Layer Perceptron Model:
It is one of the simplest Artificial neural network (ANN) types. A single-layered perceptron model includes a feed-forward network and a threshold transfer function within the model. The key objective of this model in perceptron in machine learning is to inspect the linearly distinguishable objects with binary results.
In this model, its algorithms don’t include recorded data. Hence, it starts with an inconsistently assigned input for the weight parameters. Subsequently, it adds all inputs (weight). Once all inputs are added, the perceptron learning algorithm checks the total sum of all inputs. If this sum exceeds a pre-determined value, the single-layer perceptron model is activated and displays the output value as +1.
If the output is identical to the threshold or pre-determined value, the model’s performance is mentioned as satisfied. The weight demand doesn’t alter. But this model includes certain discrepancies activated when multiple weight input values are inputted into the model. So, if you want to find the desired output and minimize the errors, there must be some changes to the weights input. Note that the single-layer perceptron model can only learn linearly separable patterns. After the single-layer model of the perceptron algorithm explained, let’s understand what a multi-layered perceptron model is.
Multi-Layered Perceptron Model:
Similar to a single-layer perceptron model, the multi-layer perceptron model also implements the identical model structure but with more hidden layers. Its alternate name is the Backpropagation algorithm. It works in two stages, as explained below.
- Forward Stage: In this stage, activation functions begin from the input layer and terminate on the output layer.
- Backward Stage: In this stage, bias and weight values are changed according to the model’s requirement. The error between actual output and demanded output creates backwardness on the output layer and terminates on the input layer.
So, a multi-layered model of perceptron learning is regarded as multiple artificial neural networks with different layers. In these layers, the activation function doesn’t stay linear but can be implemented as sigmoid, ReLU, TanH, etc.
This perceptron learning algorithm model owns more processing power. It can process both linear and non-linear patterns. Moreover, it can implement logic gates like OR, AND, XOR, NAND, XNOR, NOR, and NOT.
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Characteristics of Perceptron:
Here are the characteristics of the perceptron learning model:
- It is a machine learning algorithm for the supervised learning of binary classifiers.
- The weight coefficient is automatically learned.
- Initially, weights, and input features are multiplied. Next, the decision is made whether the neuron should be dismissed or not.
- The activation function in any perceptron learning algorithm example employs a step rule to determine whether the weight function’s value is higher than zero.
- If the sum of all input values is higher than the threshold value, it should have an output signal; else, no output is displayed.
- The linear decision boundary is plotted and it enables the distinction between the two linearly separable classes “+1” and “-1”.
Limitations of Perceptron Model:
- A perceptron model’s output can only be a binary number i.e. “0” or “1” because of the hard limit transfer function.
- The perceptron model can only be used to categorize the input vectors’ linearly separable sets. If input vectors are non-linear, they can’t be properly classified.
Future of Perceptron Model:
The Perceptron model’s future is bright because it helps to understand data by developing intuitive patterns and using them in the future. Machine learning is a swiftly developing technology of Artificial Intelligence. It is constantly evolving. So, the perceptron technology’s future will continue to support and simplify analytical behavior in machines would increase the computers’ efficiency.
The perceptron learning algorithm example states that this model is constantly getting more advanced and is efficiently working on complex problems using artificial neurons.
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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