A breakdown of the Artificial neural network concept.

The idea of an Artificial Neural Network is a concept derived from the human brain neurons called the central nervous system. The Neural Network is made up of little units, also known as nodes, which are arranged in several layers. Depending on the system's complexity, a layer can have a few dozen units or millions of units. An artificial neural network is referred to as the functional unit of deep learning which was developed to solve complex issues and also provide visual analysis of each process.

How the Human brain works (Neuron)

The Human brain is made up of neurons that are well connected and arranged into networks and patterns and communicate with one another at extraordinary rates. The human brain is made up of 100 billion neurons, The neurons are responsible for the transmission of electrical and chemical pieces of information. The human body contains a variety of neurons that perform a variety of functions. The sensory neuron is responsible for the transmission of sensory receptor cells from the body to the brain. Interneurons are responsible for transmitting information between different neurons in the body. The motor neuron is responsible for the transmission of information from the brain to the muscles of the body.

How the Artificial Neural Networks Work (Nodes)

It's made up of three layers, the input layers, the hidden layers, and the output layers The input layers are used to represent the input values passed onto the neural network. The input layers of the neural network are set to imitate the sensory neuron of the human brain. The hidden layers: this layer receives values from the preceding layer (input) and passes the neuron values to the subsequent layer, these layers are built to imitate the interneurons of the human brain. The output layer is used to represent the output value, it neuron values are used as the output, these layers are set to imitate the motor neurons. all layers are well connected that is, the input layer is connected with the hidden layers while the hidden layers are connected with the output layers. The human brain is made up of millions of neurons, while the neural network can be made up of any number of neural networks depending on the size of the project.

Types of Artificial Neural Networks

There are numerous types of neural networks, but in this course, we will focus on just a few popular ones.

Perceptron(P) Neural Networks

Perceptron is a supervised learning algorithm for binary classifiers. This algorithm was made to allow neurons to learn and process training set elements one at a time. It performs well with both small and large amounts of input data and the perceptron neurons aid us in obtaining quick predictions immediately following training. We have the Single layer Perceptron and the Multilayer Perceptron (MP).

The LSTM Memory (LSTM) Neural Networks

Long short-term memory is made up of feedback connections such as the Recurrent Neural Network (RNN) it can handle both single data points (such as pictures) and entire data streams (such as speech or video). It is made up of four neural networks and numerous memory blocks known as cells that are linked together in a chain structure. A typical LSTM unit is made up of a cell, an input gate, an output gate, and a forget gate. Lastly, we would talk about the Feed-Forward network.

Feed-forward (FF) Neural Networks

Feed-forward is a type of Neural Network that can be used Feed-forward is a relatively underutilized control strategy that can handle a wide range of process issues. they are made up of multi-layered neurons and the nodes move in a forward direction.

Convolutional Neural Network

The convolutional Neural Network is a type of neural network that is designed to process complex or structured arrays of given data. The convolutional Neural Network is made up of multiple layers which are built up with nodes, Nodes are referred to as the basic unit of a neural network Convolutional networks are widely used in computer vision

Recurrent Neural Networks (RNN)

The Recurrent Neural Network stores a layer's output and feeds it back into the input to improve layer prediction. Applications of the Artificial Neural Networks Artificial Neural Networks have added a lot of ease to the flows of so many Ease in Visual Analysis Artificial Neural network has proven to be relevant in so many industries like healthcare, Marketing, Agriculture, Finance, Transportation and so many other And most tech companies today rely on and are backed by deep neural networks. Companies like Neurala, PathAI, Speech Matics, Instagram, Facebook, etc. These companies use a neural network to perform different fascinating like Facial recognition, Interest recommendations, etc

Applications Of Neural Networks

The followings are ways in which neural networks have helped in the development of new technologies and the improvement of lives

1. Agriculture

   By assessing data from sensors and satellites to estimate agricultural productivity, improve irrigation, and recognize crop diseases, neural networks support precision agriculture.

2. Image Recognition and Computer Vision

   Convolutional neural networks (CNNs), have proven to be useful for tasks including picture classification, object detection, image segmentation, and facial recognition. Self-driving automobiles, medical image analysis, security systems, and social media platforms are just a few examples of applications.

3. Healthcare

   Medical image processing, disease diagnosis, and prognosis all involve neural networks. They aid in locating anomalies in X-rays, MRIs, and other diagnostic pictures. Additionally, they have been used in genomics studies and medication discovery.

4. Speech Recognition and Synthesis

   The development of voice recognition and synthesis technology has been made possible by neural networks. Examples include interactive voice response systems, virtual voice assistants, and speech-to-text systems. These technologies have been included in the products of companies like Amazon, Google, and Apple.

5. Financial Services

   For risk assessment, credit scoring, algorithmic trading, and fraud detection, neural networks are employed. These networks can examine massive amounts of financial data and spot trends that might be hard to find using more conventional techniques.

6. Gaming and Entertainment

   Neural networks are employed in the gaming industries to develop virtual opponents that learn and adapt to a player's strategy and also they are used to create high dimension video graphics in video games enabling more immersive experiences.

Despite having a lot of impacts Neural networks continue to encounter difficulties despite their amazing accomplishments. They need a lot of computational power, and occasionally their decision-making procedures are opaque, raising questions about bias and accountability. To improve effectiveness, fairness, and interpretability, researchers are actively addressing these issues.

Wrapping Up

If you followed this article to the end, you should now have a good grasp of what an Artificial Neural Network means, how it works, and the different types of neural networks and also understand that the artificial neural network is the backbone of modern artificial intelligence.