What Is a Neural Network and its Types?-

A neural network is defined as a software solution that leverages machine learning (ML) algorithms to ‘mimic’ the operations of a human brain. Neural networks process data more efficiently and feature improved pattern recognition and problem-solving capabilities when compared to traditional computers. This article talks about neural networks’ meaning, working, types, and applications.

What Is a Neural Network?

A neural network is a software solution that leverages machine learning (ML) algorithms to ‘mimic’ the operations of a human brain. Neural networks process data more efficiently and feature improved pattern recognition and problem-solving capabilities when compared to traditional computers. Neural networks are also known as artificial neural networks (ANNs) or simulated neural networks (SNNs).

Neural networks are a subtype of machine learning and an essential element of deep learning algorithms. Just like its functionality, the architecture of a neural network is also based on the human brain. Its highly interlinked structure allows it to imitate the signaling processes of biological neurons.

The Architecture of a Neural Network
Source: ManningOpens a new window

The architecture of a neural network comprises node layers that are distributed across an input layer, single or multiple hidden layers, and an output layer. Nodes are ‘artificial neurons’ linked to each other and are associated with a particular weight and threshold. Once the output of a single node crosses its specified threshold, that particular node is activated, and its data is transmitted to the next layer in the network. If the threshold value of the node is not crossed, data is not transferred to the next network layer.

Unlike traditional computers, which process data sequentially, neural networks can learn and multitask. In other words, while conventional computers only follow the instructions of their programming, neural networks continuously evolve through advanced algorithms. It can be said that neural computers ‘program themselves’ to derive solutions to previously unseen problems.

Additionally, traditional computers operate using logic functions based on a specific set of calculations and rules. Conversely, neural computers can process logic functions and raw inputs such as images, videos, and voice.

While traditional computers are ready to go out of the box, neural networks must be ‘trained’ over time to increase their accuracy and efficiency. Fine-tuning these learning machines for accuracy pays rich dividends, giving users a powerful computing tool in artificial intelligence (AI) and computer science applications.

Neural networks are capable of classifying and clustering data at high speeds. This means, among other things, that they can complete the recognition of speech and images within minutes instead of the hours that it would take when carried out by human experts. The most commonly used neural network today is Google search algorithms.

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How Does a Neural Network Work?

The ability of a neural network to ‘think’ has revolutionized computing as we know it. These smart solutions are capable of interpreting data and accounting for context.

Four critical steps that neural networks take to operate effectively are:

• Associating

  or training enables neural networks to ‘remember’ patterns. If the computer is shown an unfamiliar pattern, it will associate the pattern with the closest match present in its memory.

• Classification

  or organizing data or patterns into predefined classes.

• Clustering

  or the identification of a unique aspect of each data instance to classify it even without any other context present.

• Prediction,

  or the production of expected results using a relevant input, even when all context is not provided upfront.

Neural networks require high throughput to carry out these functions accurately in near real-time. This is achieved by deploying numerous processors to operate parallel to each other, which are arranged in tiers.

The neural networking process begins with the first tier receiving the raw input data. You can compare this to the optic nerves of a human being receiving visual inputs. After that, each consecutive tier gets the results from the preceding one. This goes on until the final tier has processed the information and produced the output.

Every individual processing node contains its database, including all its past learnings and the rules that it was either programmed with originally or developed over time. These nodes and tiers are all highly interconnected.

The learning process (also known as training) begins once a neural network is structured for a specific application. Training can take either a supervised approach or an unsupervised approach. In the former, the network is provided with correct outputs either through the delivery of the desired input and output combination or the manual assessment of network performance. On the other hand, unsupervised training occurs when the network interprets inputs and generates results without external instruction or support.

Adaptability is one of the essential qualities of a neural network. This characteristic allows machine learning algorithms to be modified as they learn from their training and subsequent operations. Learning models are fundamentally centered around the weightage of input streams, wherein, each node assigns a weight to the input data it receives from its preceding nodes. Inputs that prove instrumental to deriving the correct answers are given higher weightage in subsequent processes.

Apart from adaptability, neural networks leverage numerous principles to define their operating rules and make determinations. Fuzzy logic, gradient-based training, Bayesian methods, and genetic algorithms all play a role in the decision-making process at the node level. This helps individual nodes decide what should be sent ahead to the next tier based on the inputs received from the preceding tier.

Basic rules on object relationships can also help ensure higher quality data modeling. For instance, a facial recognition neural network can be instructed ‘teeth are always below the nose’ or ‘ears are on each side of a face’. Adding such rules manually can help decrease training time and aid in the creation of a more efficient neural network model.

However, the addition of rules is not always a good thing. Doing so can also lead to incorrect assumptions when the algorithm tries to solve problems unrelated to the rules. Preloading the wrong ruleset can lead to the creation of neural networks that provide irrelevant, incorrect, unhelpful, or counterproductive results. This makes it essential to choose the rules that are added to the system carefully.

While neural networking, and especially unsupervised learning, still have a long way to go before attaining perfection, we might be closer to achieving a defining breakthrough than we think. It is a fact that the connections within a neural network are nowhere as numerous or efficient as those in the human brain. However, Moore’s Law, which states that the average processing power of computers is expected to double every two years, is still flourishing. This trend gives our expectations from AI and neural networks a definitive direction.

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Types of Neural Networks

Neural networks are classified based on several factors, including their depth, the number of hidden layers, and the I/O capabilities of each node.

Types of Neural Networks

Listed below are the six key types of neural networks.

1. Convolutional neural networks

Being a highly popular neural networking model, convolutional neural networks leverage a type of multilayer perceptron and include one or more convolutional layers. These layers can be either pooled or entirely connected.

This neural networking model uses principles from linear algebra, especially matrix multiplication, to detect and process patterns within images. The convolutional layers in this model can create feature maps that capture a specific area within a visual input. The site is then broken down further and analyzed to generate valuable outputs.

Convolutional neural networks are beneficial for AI-powered image recognition applications. This type of neural network is commonly used in advanced use cases such as facial recognition, natural language processing (NLP), optical character recognition (OCR), and image classification. It is also deployed for paraphrase identification and signal processing.

2. Deconvolutional neural networks

Deconvolutional neural networks work on the same principles as convolutional networks, except in reverse. This specific application of AI aims to detect lost signals or features that may have previously been discarded as unimportant as the convolutional neural network was executing its assigned task. Deconvolution neural networks are helpful for various applications, including image analysis and synthesis.

3. Recurrent neural networks

This complex neural network model works by saving the output generated by its processor nodes and feeding them back into the algorithm. This process enables recurrent neural networks to enhance their prediction capabilities.

In this neural network model, each node behaves like a memory cell. These cells work to ensure intelligent computation and implementation by processing the data they receive. However, what sets this model apart is its ability to recollect and reuse all processed data.

A strong feedback loop is one of the critical features of a recurrent neural network. These neural network solutions can ‘self-learn’ from their mistakes. If an incorrect prediction is made, the system learns from feedback and strives to make the correct prediction while passing the data through the algorithm the second time.

Recurrent neural networks are commonly used in text-to-speech applications and for sales forecasting and stock market predictions.

4. Feed-forward neural networks

This simple neural network variant passes data in a single direction through various processing nodes until the data reaches the output node. Feed-forward neural networks are designed to process large volumes of ‘noisy’ data and create ‘clean’ outputs. This type of neural network is also known as the multi-layer perceptrons (MLPs) model.

A feed-forward neural network architecture includes the input layer, one or more hidden layers, and the output layer. Despite their alternate name, these models leverage sigmoid neurons rather than perceptrons, thus allowing them to address nonlinear, real-world problems.

Feed-forward neural networks are the foundation for facial recognition, natural language processing, computer vision, and other neural network models.

5. Modular neural networks

Modular neural networks feature a series of independent neural networks whose operations are overseen by an intermediary. Each independent network is a ‘module’ that uses distinct inputs to complete a particular part of the larger network’s overall objective.

The modules do not communicate with one another or interfere with each other’s processes while computation occurs. This makes performing extensive and complex computational processes more efficient and quick.

6. Generative adversarial networks

Generative adversarial networks are a generative modeling solution that leverages convolutional neural networks and other deep learning offerings to automate the discovery of patterns in data. Generative modeling uses unsupervised learning to generate plausible conclusions from an original dataset.

Generative adversarial networks train generative models by creating a ‘supervised learning problem’ containing a generator model and a discriminator model. The former is prepared to develop new conclusions from the input. At the same time, the latter strives to label generated conclusions as either ‘real’ (from within the dataset) or ‘fake’ (generated by the algorithm). Once the discriminator model labels the generated conclusions wrongly about half the time, the generator model produces plausible conclusions.

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Top 8 Applications of Neural Networks in 2022

From finance and social media to law and order, neural networks are everywhere today. The following are the top eight applications of neural networks in 2022.

1. Law and order

Even though their use is restricted in certain jurisdictions, facial recognition systems are gaining popularity as a robust form of surveillance. These solutions match human faces against a database of digital images. Apart from alerting authorities about the presence of fugitives and enforcing mask mandates, this neural networking offering is also useful for enabling selective entry to sensitive physical locations, such as an office.

Convolutional neural networks are most commonly used for this application, as this subtype of neural network is apt for image processing. A high volume of images is stored in the database and further processed during learning.

To ensure effective evaluations, sampling layers are used in the neural network. This helps optimize the models and guarantee accurate results. 

2. Finance

In the past, financial markets were subject to risks that were almost impossible to predict. Today, this is no longer true–neural networks have helped mitigate the high volatility in stock markets to a noticeable extent.

Multilayer perceptron neural networks are deployed to help financial executives make accurate stock market predictions in real-time. These solutions use the past performance of stocks, non-profit ratios, and annual returns to provide correct outputs.

3. Social media

In the post-pandemic world, social media has reached almost every niche of human life. Users often marvel at how social media platforms can ‘read their minds’, while in reality, they have neural networks to thank for that.

User behavior analysis is a popular application of neural networking tools. Large volumes of user-generated content are processed and analyzed by neural networks every minute. The goal is to glean valuable insights from every tap a user makes within the app. This information is then used to push targeted advertisements based on user activity, preferences, and spending habits.

4. Aerospace

Neural networking plays a critical role across the aerospace industry, from engineering to flight.

During the manufacturing process, neural networks are deployed for flawless fault diagnosis, as even the tiniest defect in an aircraft could lead to the loss of hundreds of lives.

At the operator training stage, these systems are used in modeling critical dynamic simulations to ensure that the crew is adequately aware of how real-life flights work.

Finally, during a flight, neural network algorithms bolster passenger safety by ensuring the accurate operation and security of autopilot systems.

5. Defense

With 2022 seeing an increase in geopolitical instability across Asia and Europe, proven defense solutions are becoming extremely important for every country. A robust defense posture enables a country to gain favorable recognition on the global stage.

Neural networks are playing an increasingly valuable role in the defense operations of nations with technologically advanced militaries. Neural network solutions are already being used by the militaries of the United States of America, the United Kingdom, and Japan to develop powerful defense strategies.

In the military, neural networks are leveraged in object location, armed attack analysis, logistics, automated drone control, and air and maritime patrols. For instance, autonomous vehicles powered with convolutional neural network solutions are deployed to look for underwater mines.

6. Healthcare

Image-based tests are a core pillar of the healthcare industry, leveraging the image processing prowess of convolutional neural networks to detect diseases.

This type of neural network is seen in various cutting-edge healthcare applications, including the processing of X-rays, CT scans, and ultrasounds. The data collected from the aforementioned medical imaging tests is analyzed by automated solutions to provide actionable medical insights.

Additionally, generative neural networks are being used in drug discovery research. These solutions simplify the classification of different drug categories. New drug combinations are discovered by rapidly merging the properties of various elements and reporting the findings.

7. Signature and handwriting analysis

AI-powered signature verification solutions are slowly becoming the norm in financial, administrative, and related domains. Financial institutions and bureaucracies rely on signature verification to verify the identity of end-users and prevent fraudulent transactions.

Until the last decade, analysis of signatures by human clerical staff was the standard for verifying the authenticity of documentation, making fraud easy to commit. However, with the advent of neural networks for signature verification, differentiating between genuine and forged signatures (both online and offline) has become more accessible.

Handwriting analysis is a related application of neural networks that plays a vital role in forensics. AI-backed handwriting analysis is used to evaluate handwritten documents for numerous purposes, including identity verification and behavioral analysis.

8. Meteorology

Meteorology is a vital part of daily life, helping people prepare for oncoming weather conditions in advance and even predicting the possibility of natural disasters. With neural networking entering the meteorology domain, weather forecasts become more accurate.

Convolutional neural networks, multilayer perceptrons, and recurrent neural networks are being used to boost the accuracy of weather forecasts. Multilayer neural network models are being shown to predict the weather accurately up to 15 days in advance.

Data such as relative humidity, air temperature, solar radiations, and wind speed are used to train neural network models for meteorology applications. Different neural network types are also being combined as researchers strive to forecast the weather accurately.

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Takeaways

Neural networks are a disruptive application of artificial intelligence, allowing the problem-solving powers of deep learning to be used to improve our quality of life. Neural network techniques are increasingly being used to address abstract challenges, such as drug design, natural language processing, and signature verification. As neural networks continue to become faster and more accurate, going ahead, humankind’s technological progress will be bolstered significantly.

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