What Is a Neural Network: How It Works and Why It’s Needed

Neural networks have become an integral part of people’s lives. The technology is used both visibly and invisibly. People use neural networks even if they do not attach importance to it or do not do it intentionally. To understand what neural networks are, it is worth first figuring out where they are used.

Neural Networks Around Us

Financial Services

Fraud detection: neural networks analyze transfers to identify suspicious activity. Selected questionable transactions are then reviewed by bank employees.

Stock trading: neural networks analyze market data and predict trends. They are capable of executing trades independently.

Everyday Use

Smartphones: neural networks improve photo quality, automatically process images, and control camera functions.

Advertising: optimizing advertising campaigns by analyzing data about users and their preferences.

Voice assistants: Siri, Alexa, and Google Assistant use neural networks to understand and process voice commands.

Medicine

Disease diagnosis: neural networks analyze X-rays and MRI scans to detect abnormalities. This speeds up the work of medical professionals, since it is more convenient to study a problem area in an image than the entire image.

Personalized medicine: neural network–based algorithms can process patient information. For example, genetic data and medical history. After processing, the neural network suggests a personalized treatment plan.

Telemedicine and virtual assistants: neural networks analyze patients’ symptoms and provide preliminary treatment recommendations.

Entertainment Industry

Content creation: generative neural networks create music, paintings, and even film scripts.

Video games: AI controls the behavior of NPCs — non-player characters. This creates realistic and varied gameplay.

Retail

Personalized recommendations: neural networks analyze user behavior to suggest products that may interest them. This significantly increases sales.

Supply chain optimization: algorithms study the market and forecast demand for products. This helps calculate how many goods should be kept in stock and when deliveries need to be made.

Transport and Logistics

Autonomous vehicles: neural networks process data from sensors and cameras to control vehicles without human intervention. At the time of writing, the technology is operating in test mode.

Route optimization: neural networks help determine the best delivery routes, taking traffic and weather conditions into account.

Education

Adaptive learning: neural networks analyze students’ performance. They suggest personalized educational materials based on the student’s level of preparation.

Automatic grading: processing texts and assessing the quality of written work. Checking correct and incorrect answers on tests.

How Neural Networks Work

A neural network is a computational model inspired by the human brain. They are trained on a dataset. The training process involves adjusting the weights of connections between neurons to minimize prediction error. This is similar to how the human brain learns to recognize patterns and make decisions. Ready-made models can apply their knowledge in practice. For example, by helping users.

Neural Network Structure

Neurons are the basic elements that make up a neural network. Each neuron receives input data, processes it, and passes the result to other neurons.

Layers: neurons are organized into layers. There are three main types of layers:

Input layer — receives the initial information.

Hidden layers — process data, extracting hidden patterns and dependencies.

Output layer — produces the final result of data processing.

The Training Process

Weight initialization in neural networks is the process by which each connection between neurons is assigned an initial value before training begins. These values are also called weights.

Training: a neural network is trained on data by adjusting connection weights using the backpropagation algorithm. This minimizes the difference between the network’s predictions and real information.

Generalization: after training, a neural network can process new data and make predictions. For example, answer questions or make guesses, as ChatGPT does.

Types of Neural Networks

Convolutional neural networks CNN: often used for processing images and video. They can detect patterns and objects in images, which makes them indispensable in applications such as autonomous driving and medical diagnostics.

Recurrent neural networks RNN: used to work with data sequences, for example, for text analysis or time series analysis. They remember previous data, which allows them to take context into account.

Generative adversarial networks GAN: used to generate new data, such as images or music. They work in pairs, where one network, the generator, creates data, and the other, the discriminator, evaluates how real it is.

Popular Questions About Neural Networks

What Are Hallucinations in Neural Networks?

Hallucinations are situations where a neural network generates data that does not correspond to reality. For example, it invents nonexistent facts. Or it sees objects in a photo that are not actually there.

What Is a Prompt in a Neural Network?

A prompt is the initial task given to a neural network. Precise and detailed prompts make it possible to get the best answers.

How Do You Explain What a Neural Network Is for Beginners?

Imagine a neural network as a large group of people working on one task. Each person is a neuron. They process part of the information and pass it further down the chain. This continues until the final result is produced.

Advantages and Disadvantages of Neural Networks

Advantages

Ability to Process and Analyze Large Volumes of Data

In healthcare, neural networks are used to analyze medical scans. To do this, the model needs to be trained on thousands of MRI and CT scans.

A Stanford University study showed that deep learning algorithms can detect pneumonia in chest X-rays with accuracy exceeding that of radiologists.

High Accuracy in Classification and Recognition Tasks

CNN-type neural networks are used for facial recognition. In the LFW, or Labeled Faces in the Wild, test, modern algorithms achieved 99 percent face identification accuracy. This helps modern gadgets with their security systems — device unlocking.

Automation of Complex Processes and Fast Decision-Making

In logistics, companies such as Amazon use neural networks to optimize delivery routes. Machine learning algorithms analyze real-time data on road conditions and weather conditions, making it possible to improve efficiency and reduce delivery time.

Disadvantages

Require Large Computing Resources

Training large models such as GPT-3 requires enormous computing power and energy. To train even an old version of OpenAI’s neural network, supercomputers with more than 10 thousand GPUs were used. 1.3 thousand megawatt-hours of electricity were consumed. Such a process is available only to large corporations.

Dependence on the Quality of Training Data

In 2014, Amazon encountered a problem when using a candidate selection algorithm. It turned out to be biased against women. The algorithm was trained on data reflecting historical preferences for male candidates, which led to gender discrimination.

Can Generate Errors or Hallucinations.

A study showed that neural networks can be vulnerable to hallucinations in the form of image recognition errors. For example, algorithms may incorrectly identify objects in an image when there are small changes in pixels that are invisible to the human eye.