What are Neural Networks? How Do They Work?

Neural Networks, one of the algorithms used in the deep learning subfield of machine learning, may seem incomprehensible at first glance. However, a neural network consists of a structure where the nodes in each layer cluster, the clusters overlap, and each cluster transmits data to multiple nodes in the next layer. This structure is similar to neurons in the human brain, as it was inspired by how neurons function in the brain.

What is a Neural Network?

A neural network is an information processing system consisting of simple but interconnected nodes organized in layers. This system is called artificial neural networks worldwide because it is developed similarly to how the human brain functions. Artificial neural networks, inspired by the neurons in the human brain, can be fundamentally defined as self-learning algorithms.

It is also possible to explain artificial neural networks as a computational learning system using a web of interconnected functions. These functions interpret and perceive a specific type of data input, often transforming it into a desired and understandable output.

Just as neurons in the human brain collectively work to understand inputs from human emotions and convert them into data, neural networks mimic the human brain. Each piece of data is connected via networks, and just like experiences in the human brain, the learning process is completed.

Neural networks are just one of many tools used in the vast world of machine learning algorithms. They form an integral, holistic, and collective part of various machine and deep learning algorithms used to process complex data inputs into a form that computers can understand.

Why Are Neural Networks Important?

Neural networks are important because they function similarly to neurons in the human brain and form the foundation of many AI applications. Artificial neural networks can recognize hidden patterns and correlations in raw data, cluster and classify them, and continuously learn to improve. With these features, neural networks can enhance decision-making processes in areas such as:

• Assisting with cybersecurity and fraud detection globally.
• Optimizing logistics for transportation networks through continuously learning AI applications.
• Improving natural language processing, enabling advancements in personal assistants, AI applications, and character, face, and voice recognition programs.
• Supporting medical professionals in diagnosing diseases through neural networks that extract new results from past data.
• Providing financial forecasts to assist investors by utilizing experience-based outcomes.
• Offering professional support for interpreting medical imaging, raw photos, and videos.

What is Neural Network Used For?

In the modern era, neural networks are actively used in education, finance, aviation, and the automotive industry. From face recognition to social media marketing, they support experts in various fields. Here are some examples of neural network applications:

• Face Recognition: Face recognition systems compare human faces with previously introduced images to verify identity and match them with a list in the database. Artificial neural networks make this system more effective and secure.
• Social Media Analysis: Neural networks replicate social media users' behaviors, contributing to gaining insights from their behaviors through social networks.
• Aviation: Areas like fault diagnosis, high-performance autopilot, aircraft control system safety, and basic dynamic simulations are some of the core areas where neural networks take charge.
• Defense: Neural networks play a role in logistics, armed attack analysis, air and sea patrols, and controlling automated drones. The defense industry leverages AI-powered neural networks to grow its technologies.

How Do Neural Networks Work?

Neural networks consist of multiple layers of processors that work together. The first layer receives basic input information, just as the optic nerves in the human brain receive images. Similar to neurons distant from the optic nerve, they process the data based on previous experiences. In the final layer, an output is produced based on the raw data and experiences fed into the system.

Each layer has its processing nodes, which contain a pool of information that includes what they see and all the rules they are programmed to follow. All layers are connected to form a storyline. The final output produced in the last layer, based on raw data and experiences, forms a new network that connects with the existing neural networks.

Training an artificial neural network usually involves feeding large amounts of data. At its simplest, this training provides inputs and tells the network what the desired output should be. For example, if you want to create a network that identifies bird species, your initial training data should include a series of images containing birds, non-bird animals, airplanes, and flying objects. Each input in the training should also include information like the bird's name or that the flying object is not a bird.

In machine learning, when developers define the rules and make decisions about what each node sends to the next layer based on the inputs from the previous layer, neural networks use several principles in these learnings. Some of these principles include gradient-based learning, logic, and genetic algorithms.

Types of Neural Networks

Neural networks are classified into different types based on their structure, data flow, neurons used, densities, layers, and depth activation filters. The most basic types of artificial neural networks are listed below:

Single-Layer Neural Networks (Perceptron): The perceptron model, a supervised learning algorithm that separates data into two categories, is one of the oldest types of neural networks. It is the smallest neural network unit that performs specific calculations or identifies features in input data. This model, consisting of a single neuron, accepts inputs and uses an activation function to produce a final result.

Multilayer Neural Networks: These networks, also known as "feedforward neural networks," consist of at least one hidden layer in addition to the input and output layers. The hidden layers identify the features of unprocessed data and guide the results to the output layer.

Recurrent Neural Networks (RNN): Recurrent neural networks are more complex by nature. These networks save the outputs of processing nodes and store the results for the next round of processing. Each node in a recurrent neural network acts as a memory cell that maintains the processes.

Convolutional Neural Networks (CNN): One of the most popular models, CNNs use a variation of multilayer perceptrons. They consist of one or more convolutional layers that can be fully connected and easily assembled together. CNNs are often used for image recognition in advanced AI applications.

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