In this article, we are going to study different applications of neural networks and their types. In the fields of AI, machine learning, and deep learning, neural networks enable computer programs to identify patterns and resolve common issues by mimicking the behavior of the human brain. A computational network based on biological neural networks, which create the structure of the human brain, is typically referred to as an artificial neural network.
Different Applications of Neural Networks and their Types
An artificial neural network (ANN) in information technology (IT) is a hardware and/or software system designed after how neurons in the human brain function. Machine learning is a subset of neural networks. Similarly, Simulated neural network (SNNs) or Artificial neural network (ANNs) refers to deep learning techniques.
There are three layers in a neural network the input layer, the output layer, and the hidden layer which consist of one or more layers. Each node has a weight and threshold associated with it, and they are all connected to one another.
Google’s search algorithm is one of the well-known neural networks.
The best moment to learn about neural networks, machine learning, and artificial intelligence applications is right now because many new applications are being implemented daily. Below are some of the applications discussed:
- Image compression: The goal of neural network data compression is to replicate the original image by storing, encrypting, and compressing it once again. Therefore, by implementing picture compression neural networks, we can reduce the amount of data we need to store. It is the best solution to optimize and conserve memory.
- Issues with travel for salespeople: This application deals with determining the best route between cities in a specific region. Neural networks assist in resolving the issue of generating greater revenue at minimal expense. However, We need to identify the best travel routes for salespeople going from town to town because the logistical considerations are so great.
- Stock-Exchange prediction: The stock exchange is impacted by a wide range of events, making it challenging to monitor and comprehend. However, Stockbrokers would benefit from a neural network’s ability to look at many of these variables and forecast the prices on a daily basis.
- Handwriting Recognition: Handwritten characters are transformed into digital characters that a computer can read using neural networks.
The number of layers between the input and output, or the model’s “hidden layers,” is commonly used to describe the depth of neural networks. However, artificial neural networks are available in a variety of types, including:
Data movement between the input and output nodes occurs in one direction in a feedforward artificial neural network. Data doesn’t cycle back through the same tiers of nodes; it travels ahead through them. Although there may be numerous layers and numerous nodes, Feedforward neural networks are generally easy to understand because of the one-way data flow.
The perceptron is one of a neuron’s earliest and most basic representations. In other words, a perceptron model is a binary classifier that divides data into two categories. A linear model is one of the primary uses of an artificial neural network.
A multilayer perceptron artificial neural network can have multiple hidden layers between the input and output, increasing complexity and density. Every single node on one layer is linked to every node on the following layer. Multilayer Perceptron models can be used for deep learning because they are completely connected networks.
This type of neural network generally consists of three layers, the input layer, the output layer, and a layer of function nodes with different parameters. Models can be used for system control, time series regression, and categorization. Radial basis function neural networks are frequently used in systems that handle power restoration following a power outage.
Recurrent neural networks are effective tools when a model is created to process sequential data. To best accomplish a task and enhance predictions, the model will transfer input forward and loop it back to earlier stages in the artificial neural network.
Similar to feedforward artificial neural networks in terms of data flow, each node will hold onto the knowledge required to advance each step. Sequence to sequence models that are used for natural language processing, also incorporate recurrent neural networks.
A modular artificial neural network is made up of a number of networks or components that operate independently but cooperatively to complete a task. Thus, it is possible to divide a difficult work into smaller parts. Smaller components can collaborate with one another to speed up processing when used in computing or data processing.
Artificial neural networks are made to mimic the behavior of neural networks in the brains of humans and other animals. For instance, machine learning acquires the model architecture needed to handle increasingly complicated data by mimicking and modeling the behavior of neurons. Artificial neural networks come in a wide variety of forms, with many early incarnations appearing straightforward in comparison to contemporary methods.
Neural networks can seem difficult to understand and master. Therefore they are in many ways. As we have observed, many aspirant data scientists and deep learning enthusiasts have given up before they have even used a neural network. However, they’ve approached it incorrectly. Numerous misconceptions about neural networks drive people to give up like you need a Ph.D. to understand neural networks, and you require an understanding of complex mathematics to construct a neural network.
This model’s major goal is to learn by autonomously changing itself so that it can eventually perform difficult tasks that are impossible to accomplish using traditional rule-based programming.
The following are the benefits of artificial neural networks.
- Organic learning is possible for neural networks. In other words, it implies that the inputs and outcomes provided to an artificial neural network originally by an expert system are not the only constraints on its outputs.
- Nonlinear systems are capable of avoiding solutions that require a lot of computation. Instead of waiting for records in a data source to be explicitly connected, these systems can also infer relationships between data points.
- The fault tolerance of artificial neural networks has the potential to be quite high.
- However, artificial neural networks have the ability to regenerate enormous volumes of data through inference and assist in identifying the malfunctioning node.
The following are 5 examples of various artificial neural network types:
- Feedforward artificial neural networks
- Perceptron and Multilayer Perceptron neural networks
- Radial basis function artificial neural networks
- Recurrent neural networks
- Modular neural networks
The three important sectors that neural networks regulate are finance, healthcare, and the auto industry. Here are the following applications of a neural network.
- Facial Recognition
- Stock Market Prediction
- Social Media
While neural networks are effective as a function approximator, they can be integrated with other AI techniques to do more difficult tasks to reach a broader intelligence. The machine learning technique known as deep learning makes use of artificial neural networks. Since models make use of an artificial neural network’s “deep,” multi-layered design, the process is known as deep learning.