PROPERTIES OF CONVOLUTIONAL NEURAL NETWORKS

Аннотация

Сверточные нейронные сети широко используются в искусственном интеллекте и машинном обучении при анализе изображений, обработке естественного языка и другой сложной обработке данных. Основными характеристиками CNN являются автоматическое обучение признакам, извлечение важных элементов из изображений и данных, инвариантность, иерархическое обнаружение признаков и эффективное совместное использование параметров. В этой статье представлена подробная информация о принципе работы сверточных нейронных сетей, их слоях (свертка, объединение, полная связность), а также о том, как они используются в реальных задачах. Ключевые слова: Сверточная нейронная сеть, искусственный интеллект, области машинного обучения, обработка естественного языка, Выпуск журнала №-26 Часть–6_ Май –2025 205 MODERN EDUCATION AND DEVELOPMENT изображения и данные, инвариантность, иерархический признак, нейронная сеть, слои, свертка, объединение, полная связность. In the fields of artificial intelligence and machine learning, convolutional neural networks (CNNs) play a very important role. They are mainly used in image processing and classification, but are now also used in natural language processing, voice data, and many other areas. CNNs differ from other neural networks in their structure and operating principle, as they are multilayered and adapted for automatic feature learning. This article analyzes the main characteristics of convolutional neural networks and their operating principles. Basic concepts of convolutional neural networks. Convolutional neural networks are a special type of artificial neural networks, the main purpose of which is to automatically learn features from images or other data sets. CNNs often consist of the following parts: Convolutional Layers. The most important component of a CNN is the convolutional layers. These layers divide the image into small parts and analyze each part independently. Each convolution layer contains a small matrix called a "kernel" or "filter" that operates on each pixel of the image. The purpose of the filter is to extract important features in the image (such as edges, corners, structures, etc.). Activation Function. After the convolution operation, the results are passed through an activation function. The most commonly used activation function is ReLU (Rectified Linear Unit), which activates a specific feature, and if it is negative, it is equal to 0. The ReLU function helps reduce the uncertainty of the model and speed up the calculation. Pooling Layers. After the convolution layer, pooling layers are often used. Pooling is mainly used to reduce the dimensions of the image, which reduces the calculation time and is effective in training the model. Techniques such as max pooling or average pooling help in extracting important features from an image. Max pooling, for example, preserves the important features of an image by selecting the largest value within a small portion of the image. Выпуск журнала №-26 Часть–6_ Май –2025 206 MODERN EDUCATION AND DEVELOPMENT Fully Connected Layers. At the end of a CNN, there are fully connected layers. These layers connect each neuron to all neurons in the network and are used to make final decisions. Their job is to transform the features extracted from the image into the final classification or regression result. Normalization. Techniques such as batch normalization are used for CNNs to speed up learning and increase the generalization ability of the model. This technique normalizes the data at each layer of the network, so that the learning process of the network becomes more stable and efficient. Features of Convolutional Neural Networks. Automatic feature learning. The biggest feature of CNNs is that they can automatically learn features from images or other data. This is useful, for example, in image classification, because the network learns the specific features in the image, such as edges, colors, textures, and other important elements. As a result, CNNs do not require much manual feature extraction, and they are very effective in solving complex classification problems. Invariance. CNNs are invariant to the dimensions, rotations, and stresses in images or other data. For example, even if the same image is shown from different angles, a CNN can still be highly accurate in identifying it. This increases the network's generalization ability and provides flexibility to various changes. Hierarchical feature learning. CNNs often have a multi-layer architecture, with different features detected in each layer. The initial layers detect simple features (such as edges and corners), while subsequent layers combine these features and learn more complex features. This allows you to create a hierarchical structure, which is important for object recognition in an image. Dimensionality reduction. The use of pooling layers in a convolutional network reduces the dimensions of the image, which increases computational efficiency. Pooling is especially useful