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Artificial Neural Networks: Simply explained

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    Neural Networks, Künstliche Neuronale Netzwerke, ein menschliches Gehirn im Zentrum eines schematischen Netzwerks
    Alexander Thamm GmbH 2025, GAI

    Artificial neural networks (ANNs) are one of the central technologies in the field of artificial intelligence and machine learning. Inspired by the human brain, they have proven to be powerful tools that can handle complex tasks such as image and speech recognition, autonomous driving, personalized recommendations, and fraud detection. With the ability to analyze huge amounts of data and extract patterns from them, artificial neural networks play a crucial role in numerous industrial and scientific applications. Different types of neural networks are used, each tailored to specific requirements and significantly increasing the efficiency and accuracy of the systems.

    What are artificial neural networks?

    Artificial neural networks are models of artificial intelligence that are based on the functioning of biological brains. They can be assigned to the subfield of machine learning (ML for short) that aims at automated pattern recognition and data analysis. Artificial neural networks are among the advanced algorithms within so-called “deep learning” – a specific type of machine learning. In particular, deep (multilayer) neural networks are used to recognize complex data structures and patterns.

    An artificial neural network consists of numerous nodes or “neurons” arranged in layers. This multitude of layers is what gives “deep learning” its name. The neurons simulate the processing units in the brain and learn to recognize and process connections in large amounts of data. Compared to conventional machine learning methods, which are often based on only a few features, neural networks enable automatic feature extraction and processing, making them particularly suitable for complex applications such as image recognition or speech processing.

    How artificial neural networks work

    Artificial neural networks consist of three main components: the input layer, the hidden layers and the output layer. These layers are connected by neurons or nodes, each of which has specific weightings and is controlled by so-called activation functions.

    Input layer:

    The raw data is fed into the network in the input layer. Each neuron in this layer represents an input variable and serves as the starting point for information processing. For example, an image can be fed into the input layer in the form of pixel values.

    Hidden layers:

    The hidden layers are responsible for processing and transforming the input data. These layers perform the calculations that enable the network to recognize patterns and process complex relationships. Each connection between the neurons carries a weighting that determines how strongly the output of one neuron influences the next. During forward propagation, the data is passed through the network. In the so-called backpropagation process, the errors during training flow backwards through the network to adjust the weightings by strengthening or weakening them and thus optimizing them. To perform backpropagation or error feedback, an external teacher must be available during training who knows the desired target value for each input. The error to be minimized describes the deviation between the desired target value and the achieved output value of the artificial neural network.

    Output layer:

    The output layer provides the final result of the network. Depending on the application, the output can be a value, a class or a probability distribution. For example, an artificial neural network in a classification problem could predict whether an image represents a cat or a dog.

    The learning process of an artificial neural network is based on optimizing the weightings and minimizing the error as described above through iterative adjustments. This optimization is often done using “gradient descent,” a method in which the errors in the weightings are corrected step by step to find the best possible solution. Thanks to this self-learning ability, artificial neural networks are able to learn from large amounts of data and solve complex tasks.

    Types of artificial neural networks

    In recent years, artificial neural networks have become highly diversified in order to meet specific requirements and data structures. This has resulted in different types that are tailored to specific tasks:

    Feedforward neural networks (FFNN)

    Feedforward networks are the most basic form of artificial neural network. Information is passed in only one direction, from the input layer to the output layer via one or more hidden layers. In each neuron of the hidden layer, a weighted summation of the inputs takes place. This sum is processed by an activation function and passed on to the next layer. There is no feedback, which makes the network structure simple and well suited for applications such as classification and simple pattern recognition.

    Convolutional Neural Networks (CNN)

    Convolutional Neural Networks are specifically designed for processing and recognizing visual data. These networks, called convolutional neural networks, use convolutional layers to recognize spatial hierarchies in images. They are based on the idea of convolution, a mathematical operation that makes it possible to extract features such as edges, textures and complex shapes from image data. In addition to the convolutional layers, pooling layers are used to reduce the size of the data and thus the computing power by “compressing” the image while preserving important features. Convolutional neural networks are particularly successful in areas such as image recognition, medical image analysis and video processing.

    Recurrent neural networks (RNN)

    Recurrent neural networks are networks with a feedback that enables them to store and process information from previous states. This makes them particularly suitable for time-dependent data and sequence tasks, such as speech processing and time series analysis. However, the learning process in recurrent neural networks is often challenging due to the feedback loops and requires special optimization processes to avoid the so-called “vanishing gradient problem”. This problem occurs mainly in networks with numerous hidden layers. A popular example of the use of recurrent neural networks is text generation, in which the network uses feedback to take into account the context of previous words in order to generate meaningful continuous text.

    Long Short-Term Memory (LSTM)

    Long short-term memory networks are an advanced form of recurrent neural networks that were developed especially for long-term dependencies in sequences. They can store data over longer periods of time and thus respond better to tasks that require contextual information, such as translating natural language or understanding the context in long texts. Thanks to special memory units called “cells” and their “gates”, long-term short-term memory networks can focus on relevant information and ignore unimportant information. This ability is designed to circumvent the “vanishing gradient problem” of recurrent neural networks described above.

    Applications

    Artificial neural networks have already become established in a number of industries, where they solve complex problems, recognize patterns in large amounts of data and make predictions:

    Image processing

    In image processing, convolutional neural networks are particularly crucial for applications such as facial recognition, medical image analysis and object recognition. They are usually trained to identify specific patterns in images. After preprocessing the image data, features are extracted using convolutional layers to identify the individual features. After that, the image size is often reduced by so-called pooling, so that only the relevant information remains. Finally, the data is classified and interpreted. In healthcare, for example, assisted image analysis using artificial neural networks helps doctors to detect tumors or anomalies in MRI scans by training the network on a variety of images that are classified as pathological or healthy. Using convolutional neural networks to recognize such patterns has significantly improved diagnostic speed and accuracy, helping to ensure that life-saving treatments are initiated more quickly. 

    Speech recognition

    Recurrent neural networks and long-term short-term memory networks are particularly useful for speech recognition and processing because they can recognize temporal dependencies in speech data. Similar to their use in image processing, the data is also preprocessed during the speech processing. The artificial neural networks then analyze the audio signal in a temporal sequence, whereby earlier parts of the sequence are taken into account when processing the following parts. This is necessary to understand the context in coherent sentences. In the last step, the content is recognized and classified again. This enables fast and efficient communication between humans and machines and thus provides an intuitive and user-friendly interface for a variety of applications. As a result, these networks are used, for example, in digital assistants such as Siri, Alexa or Google Assistant to convert spoken language into text in real time and provide context-based responses. 

    Autonomous driving

    In the field of autonomous driving, artificial neural networks play a central role because they are responsible for the real-time analysis of data from various sensors and cameras. To do this, autonomous driving systems combine several neural networks. The sensors of an autonomous vehicle (e.g. cameras, lidar, radar) continuously collect environmental data, which is then analyzed by convolutional neural networks to identify objects and traffic situations. This information is transmitted to control networks, which decide how the vehicle should react. The use of artificial neural networks in autonomous vehicles thus increases driving safety because they are able to recognize and analyze situations faster and more reliably than a human. This can reduce the risk of traffic accidents and could, in the long term, help to improve mobility for people who may not be able to drive a vehicle themselves.

    Fraud detection and finance

    In the finance industry, artificial neural networks can help detect fraudulent activities in real time, analyze credit risks and predict market developments. Recurrent neural networks are used for this purpose, trained to identify atypical behavioral patterns. They can analyze sequences in data and thus detect unusual developments in real time. Real-time detection of fraudulent activities protects banks and their customers from financial losses. The networks are constantly learning and adapting to new fraud strategies, which makes the financial system more secure and efficient. In addition, automated risk analysis using artificial neural networks improves lending by enabling potential risks to be more accurately assessed and classified.

    Translation services and language models

    Artificial neural networks are widely used in natural language processing (NLP), particularly in translation services such as Google Translate or language models such as ChatGPT. Recurrent networks are often used here, which can analyze language structures and generate context-sensitive translations and responses. This involves analyzing text data at multiple levels, taking into account word relationships and context in large amounts of text. Long-term short-term memory networks allow the model to grasp the meaning of words in context in order to understand and produce natural language. The translation services use these networks to automatically recognize the input language, reproduce it in the desired target language, and ensure grammatically and contextually meaningful translations. Artificial neural networks thus enable fluent communication between different languages and are essential in the globalized world, as they lower language barriers and promote the exchange of knowledge.

    Conclusion

    Artificial neural networks have become an indispensable technology that is permanently changing the way we live and work. Thanks to their ability to recognize complex patterns in data and make decisions in real time, they increase efficiency and precision in a wide range of areas – from medicine and autonomous driving to financial services and customer service. The versatility and adaptability of artificial neural networks make them a central component of modern AI systems that offer solutions to some of the major challenges of our time. The variety and specialization of network types show that they can be flexibly tailored to a wide range of application fields, significantly increasing their efficiency and effectiveness.

    Author

    [at] Editorial Team

    With extensive expertise in technology and science, our team of authors presents complex topics in a clear and understandable way. In their free time, they devote themselves to creative projects, explore new fields of knowledge and draw inspiration from research and culture.

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