The Architecture of Artificial Neural Network
In this article, I am going to discuss the Architecture of Artificial Neural Networks (ANN). Please read our previous article where we discussed Model Tuning Techniques in Machine Learning.
Artificial Neural Networks
A computational model based on the structure and operations of biological neural networks is known as an artificial neural network. Computers are unable to comprehend the context of real-world situations the way that human brains do. These neural networks are mostly employed for forecasting and prediction applications. In order for the computer to learn things and make decisions in a way that is similar to that of a human, artificial neural networks try to duplicate the network of neurons that constitute the human brain.
By considering the qualities of the brain, you may quickly comprehend the characteristics of an ANN.
- Can be taught to handle data
- Can comprehend erratic data
- Image identification
- Able to generalize
How are the human brain and neural networks similar?
According to biology, learning takes place in the human brain. A neuron is one of the brain’s primary components. Information is transmitted via neurons. Information is communicated between various brain regions and between the brain and the rest of the nervous system using electrical impulses and chemical signals. A cell body and two extensions known as an axon and a dendrite make up the three basic components of a neuron. A neuron receives information or signals via dendrites, analyses them in the cell body, and then sends the developed signal to neighboring neurons via an axon. The signal is produced at the axon in the form of neurotransmitters, which the receiving neuron’s dendrites will take in.
Synapses are where neurotransmitters are released and captured. According to biology, learning occurs when the synaptic strength of individual neurons changes. We refer to neurons as being tightly bound and having high synaptic strength if the receiving neurons almost entirely capture the releasing neurotransmitters. They are referred to as loosely bound and weak synapses if the releasing neurotransmitters are only marginally caught by the other neurons. According to biology, learning is a process of learning to construct a knowledge model in which synaptic strength is updated. The connectivity between nodes is modified in ANN to create knowledge models that are as biological as possible.
The Architecture of Artificial Neural Network (ANN) –
Let’s first examine the construction of ANNs in order to comprehend how they function. There are three crucial layers in a neural network:
- Input Layer: The input layer, which is the top layer of an ANN, is where input data in the form of letters, numbers, audio files, image pixels, etc. is first received.
- Hidden Layer: The hidden layers are located in the center of the ANN model. One hidden layer, like in the case of a perceptron, or several hidden layers are both possible. On the input data, these hidden layers run a variety of mathematical operations and identify the patterns that are there.
- Output Layer: The middle layer’s meticulous computations enable us to produce the desired result in the output layer.
ANN employs a variety of mathematical processing stages. It has a number of units arranged in a number of layers. A single unit is known as a Neuron. The input layer’s input units take in a variety of inputs from the outside environment. The data then travels to the hidden unit, which transforms it into a format that output units may use.
A neuron begins by adding the values of all the neurons in the layer below it to which it is linked.
The figure below shows a single input layer with four input units, two hidden layers, the first of which has four neurons and the second of which has three, and a single output layer with two output units.
In the next article, I am going to discuss Activation Functions in Artificial Neural Networks. Here, in this article, I try to explain the Architecture of Artificial Neural Networks. I hope you enjoy this Architecture of Artificial Neural Network article. Please post your feedback, suggestions, and questions about this Architecture of Artificial Neural Network article.
About the Author: Pranaya Rout
Pranaya Rout has published more than 3,000 articles in his 11-year career. Pranaya Rout has very good experience with Microsoft Technologies, Including C#, VB, ASP.NET MVC, ASP.NET Web API, EF, EF Core, ADO.NET, LINQ, SQL Server, MYSQL, Oracle, ASP.NET Core, Cloud Computing, Microservices, Design Patterns and still learning new technologies.