Neural network architectures

Neural Network Architectures

For AI to work like the human brain, it must consist of many interacting artificial neurons, combined into groups. Modern neural networks are able to recognize images, create new images, generate texts, make predictions and perform many tasks that were previously impossible to do without the participation of the human thought process.

But the artificial brain will not work correctly without deep learning. Imagine that you are teaching a child to distinguish a watermelon from a ball. You show him images of these objects, explain the differences. And over time, his neurons build connections that help to accurately recognize where the ball is and where the watermelon is, not only in these pictures, but also in any other images. This is how AI learns to remember patterns and solve problems.

Deep learning models require neural network architectures that imitate the biological behavior of the brain. They consist of layers of nodes connected to each other to process data. The main components of a neural network: input, hidden, output layers, neurons, weights, activation and loss functions.

The purpose of this article is to give the reader an idea of ​​the various neural network architectures, their evolution and practical application. We will consider classical and modern architectures, their features, examples of use in real problems. We will discuss the prospects for the development of AI and the role of neural networks in this process.

Classical neural network architectures

Basic neural networks are built on the basis of mathematical principles developed in the mid-20th century. They serve as a basis for more complex models and have become the foundation for deep learning. Their main characteristics are: fixed structure, direct signal propagation, simple learning algorithms. Although classical neural networks are already outdated, many of them are still used to solve simple problems.

List of classical architectures:

• Perceptron is one of the first neural network models that formed the basis of machine learning, consists of one layer of neurons, can solve simple linear problems.
• MLP is a more advanced version of the perceptron, consisting of three main layers with the ReLU or sigmoid activation function.
• Convolutional networks are a type of feedforward networks for image analysis and language processing. CNNs use convolutional layers to automatically extract features.
• Recurrent networks are networks with feedback for processing data sequences (text, time series), subject to the vanishing gradient problem.
• Self-organizing maps are neural networks that learn without a teacher, creating clusters from a set of input data, which reduces their dimensionality.
• Autoencoders are continuously learning models using backpropagation of errors, which restore the original structure of the input data.
• Restricted Boltzmann machines are a two-layer neural network with an input and a hidden layer. Due to the complexity of the calculations, nodes in RBM are connected only between layers. * Deep belief networks are a standard architecture of a multi-layer network consisting of several RBMs, but with a new learning algorithm that occurs in two stages: unsupervised and supervised fine-tuning.
• Deep stacking networks are a traditional framework consisting of a set of deep individual networks, each with its own hidden layers. It is used to train deep models.

Classic neural network architectures became the basis for subsequent research. But with the growing complexity of problems and data volumes, there was a need to develop more advanced models that can effectively work with deep structures.

Modern neural network architectures

Modern architectures significantly surpass classical models in depth, complexity, and efficiency. They use advanced mechanisms for communication between neurons, are able to learn on large amounts of data, and solve complex problems. The latest architectures automatically identify hierarchical features, adapt to changing conditions, and demonstrate high prediction accuracy. Modern networks are constantly evolving in the field of deep learning.

The main types of modern architectures:

• Transformers – based on the attention mechanism (Self-Attention), do not require recurrent connections, which speeds up training. Transformers are often used in natural language processing (for example, BERT, GPT).

• Generative Adversarial Networks – generative models that consist of two networks (generator and discriminator). GANs are used to synthesize images, videos, and other visualized data.

• Attention Networks – neural networks that focus on important information. The mechanism of action excludes irrelevant data and focuses only on the necessary information upon request. Suitable for intelligent translation and natural language processing.

• ResNet is an innovative architecture that stands for “backup network”. It consists of many variants based on the concept of passing data through several layers without gradient attenuation.

• Capsule Networks are improved CNNs with an improved structure due to capsules, dynamic routing, connection coefficients and margin loss. The result is a better representation of spatial relationships between objects.

• Kernel Associative Networks (KAN) are a new type of networks with a kernel function for working with associative memory and perception of constantly changing reality. It can be compared with the memories stored by the human brain: strengthening associations, fading in the memory of the past, the ability to track constantly changing objects and update information.

• Graph neural networks are models developed for working with graphs and structured data. They are used in social network analysis, bioinformatics.

The list of modern neural networks may be incomplete, since developers create more advanced models based on previous versions or using completely new technologies almost every day.

Examples of use

Private companies and government agencies have been using the achievements of IT technologies for work for many years. Applications and software based on artificial intelligence have found application in various industries - from e-commerce to the creation of unmanned vehicles. No one is surprised by food delivery robots that travel along specified routes, or cars that drive without a driver and are able to instantly make decisions in non-standard situations.

The main areas of use of neural networks:

• Customer support - chatbots created on the basis of AI have long been communicating with users, helping to solve technical problems, giving informative answers, accepting orders. Often, customers do not even suspect that they are communicating not with an operator, but with a robot.
• Telecommunications - manage ATMs, automated information services, payment processing systems. They decipher handwritten text, perform routing, control, monitor networks, recognize faces, fingerprints, process signals, understand speech.
• Aerospace industry - find and fix technical failures of aircraft components, model trajectories and control flights without pilots.
• Automotive industry - used to develop autonomous cars, recognize objects on the road, predict the actions of other road users and make decisions in real time.
• Energy - analyze data and weather conditions for optimal distribution of energy in networks, control the operation of distribution systems and prevent interruptions.
• Pharmaceuticals - predict which molecules will be more effective for creating new drugs, process medical images (X-ray, ultrasound, MRI) to improve diagnostics.
• Agriculture - based on weather and soil analysis, give recommendations on planting crops.
• Financial industry - predict changes in stock markets, optimize trading strategies, analyze transactions and user behavior to identify suspicious transactions.

These examples show how deeply neural networks have penetrated into various areas of professional activity. But they have become no less widespread among ordinary users. AI services write texts, create realistic images, synthesize videos, music, speech, and provide structured necessary information.

Conclusions and prospects for the development of AI

From simple single-layer models to complex architectures, neural networks have come a long way. But even now, artificial intelligence is far from the human brain. And at this stage, AI development continues. The prospects include improving the interpretability of models so that they can justify their decisions, regulating ethical and social aspects, and integrating with quantum computing to improve productivity. In general, significant breakthroughs in artificial intelligence and machine learning are expected in the coming years.

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